Medical Laboratories in Healthcare Delivery: A Systematic Review of Their Roles and Impact

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REVIEWER 1 COMMENTS

AUTHORS’ RESPONSES

1

The article lacks a clear problem definition

Thanks for your observations.

Statements of problems have been clearly defined and included in the manuscript Lines [77-102]

The following problem statements necessitated the scoping review, "Medical Laboratories in Healthcare Delivery: A Scoping Review of Their Roles and Impact."

Knowledge Gap in Laboratory Contributions: There is a significant lack of comprehensive understanding regarding the full scope of roles that medical laboratories play in the overall delivery of healthcare. This gap hinders the ability to optimize lab-based diagnostics and treatments, critical to patient outcomes.

Impact Measurement Challenges: The impact of medical laboratories on healthcare delivery is not consistently or adequately measured. Without clear metrics and evaluation frameworks, it is difficult to assess how effectively labs contribute to patient care and health system efficiency.

Integration Issues: Medical laboratories often face challenges in integrating their services with other healthcare delivery components. This disjointed integration can lead to inefficiencies, delays in diagnostic processes, and potential negative impacts on patient care.

Resource Allocation and Utilization: There is a need to investigate how resources are allocated and utilized within medical laboratories. Inefficiencies in resource management can affect the quality and timeliness of laboratory services, ultimately impacting healthcare delivery.

Technological Advancements and Adoption: The rapid pace of technological advancements in laboratory sciences creates both opportunities and challenges. There is a need to evaluate how new technologies are being adopted, their impact on laboratory performance, and their contribution to improved healthcare outcomes.

Workforce Challenges: The role of the laboratory workforce in healthcare delivery is often underappreciated and understudied. Understanding laboratory personnel's training, workload, and professional development needs is crucial for enhancing their contribution to healthcare.

2

The authors write: “This scoping review examines the multifaceted contributions of medical laboratories, emphasizing their importance in pandemic preparedness, disease surveillance, and the integration of innovative technologies such as artificial intelligence (AI).” This is very broad and at several places in the article other topics, not coming from the review pop up.

Thank you for the observation.

The manuscript has been modified such that the Artificial Intelligence (AI) section focuses on a specific but relevant sub-title - “The Roles of Artificial Intelligence Technologies in Modern Laboratory Medicines. Please see Line [421]

The manuscript has been streamlined with specific research questions, objectives, and goals. The use of AI responded to research questions 3, 4, and 5. Please see Lines [422-423]

Adoption of Artificial Intelligence (AI) is a 3-fold approach to resolving research questions 3,4 and 5. [Line 422-423]

3

The authors set as their goal: “This research aims to develop a cost-effective model for quality management systems in MLS in low-income countries, contributing to sustainable healthcare systems.” How this relates to the problem definition is not made explicit by the authors and in the end no cost-effective model for quality management in MLS for low-income countries is developed.

Many thanks for your observations.

 The cost-effective model for a quality management system in MLS for LMICs was originally enumerated in the manuscript. Please refer to [Line 374 -420]

However, I have introduced an additional paragraph to clarify the ten (10) key elements constituting a cost-effective model. Please see [Line 362 -373]

  3.4 Quality Management System in Medical Laboratories- A Cost-effective Model

This article proffers a cost-effective model for QMS in MLS and LMICs that contribute to sustainable healthcare systems. Quality management systems (QMS) in MLs are essential to ensure test results' accuracy, reliability, and timeliness. Implementing a QMS in line with ISO 15189: 2022 requirements for public health laboratories ensures the validity of test results and guarantees customers' satisfaction. Comprehensive QMS, such as ISO 15189, outlines requirements for quality and competence in MLs. This cost-effective model ensures a systematic approach to quality management, covering all aspects of laboratory operations. Here are some key elements of ISO 15189: 2022 QMS, practices, and strategies that laboratories use to maintain high QC standards. The key elements listed below constitute a cost-effective model for QMS in MLS and LMICs that contribute to sustainable healthcare systems. [Line 362 -373].

4

If this is a review article, then the method/strategy for searching, selecting, and evaluating articles is lacking.

Thanks for the observation.

I have incorporated a well-defined methodology/strategy for searching, selecting, and evaluating selected articles using the PRISMA extension for scoping review. Please see [Line 134-206]

2.1 Methodology

The method used for this article is scoping reviews, a type of knowledge synthesis, that follows a systematic approach to map evidence on a topic and identify main concepts, theories, sources, and knowledge gaps. The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) was adopted. The PRISMA-ScR (PRISMA extension for Scoping Reviews) was developed according to published guidance by the EQUATOR (Enhancing the Quality and Transparency of Health Research) Network for the development of reporting guidelines [57].

We evaluated studies for the roles and impact of MLS in public health delivery. The aim was to assess the primary outcomes, strengths, and shortcomings of the collected studies by analyzing selective information based on identified criteria and parameters by evaluating the defined goal and scope of the study, the roles of MLS, the impact in healthcare, and the main limitations, conclusions, and recommendations. Overall, we aimed to evaluate the roles and impact of MLS in healthcare service delivery.

To achieve this aim, this review was organized as follows: The introduction provides a background and context of MLS, particularly in LMICs. The next section presents the methodology used to screen the information from the database and the main areas of the scooping review and meta-analysis. The gathered data from the scooping review was summarized in line with the research questions, goals, and objectives and reported as results. Based on the analyzed information, recommendations and future direction were documented.

2.2 Methodology and strategy for searching and filtering the literature

Databases, keywords, and strategy criteria: The meta-analysis was conducted based on the concept of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology for the assessment and identification of relevant and targeted bibliography when scoping reviews [57]. An initial criterion was followed for source identification based on two main overall concepts: Laboratory medicine and healthcare delivery.

A systematic screening was conducted in the primary databases of the platforms Web of Science and Scopus and other search engines, including ResearchGate and ProQuest, for scientific publications in scientific journals. Furthermore, PubMed websites, consultant group websites, and Google Scholar were accessed for screening, robust checking, and identification of reports unavailable in primary databases. The search keywords were medical laboratory, laboratory medicine, healthcare service delivery, the impact of laboratory science, medical laboratory in LMICs, laboratory medicine, emerging technologies, and artificial intelligence, amongst others.

The main criteria for filtering (exclusion/inclusion) the results were works included in journal publications, publicly available reports, theses, conference proceedings, and class projects. The selected studies' time frames were from January 2001 through November 2024, and they focused on Africa, Asia, North America, and European regions and the English version of published works.

The PRISMA logical model was used for the overall data collection. Initially, using the combinations of keywords resulted in c. 20,000 records from the selected databases. The second screening was done to identify duplicate studies (e.g., conferences and papers), resulting in c. 10,000 records. The third screening involved excluding records on title, abstract, and relevance (e.g., not full report available but a summary, reviews/perspectives articles, studies related only to the product without analysis of the packaging system, and language), resulting in c. 400 studies for eligibility. The final screening, where a more exhaustive set of criteria were applicable, such as meeting the criteria of geographical boundaries and results reported as absolute values rather than in relative %, among others, resulted in the final group of studies for inclusion in the analysis and further processing of the information provided in this work. Studies included and meeting the main criteria led to 58 studies for this meta-analysis.

2.3. Methodology for analyzing the reported information

The selected papers collected and classified according to the PRISMA methodology were analyzed and reviewed in line with the research questions, goals, and objectives.

After all the information was extracted and analyzed, comparison and further cross-analysis among studies and the main analyses for research goals were conducted. The main gaps and recommendations were outlined. Overall, the meta-analysis was focused on answering the following specific questions.

•           What are the primary roles and functions of medical laboratories in the healthcare delivery system?

•           How do medical laboratories impact patient outcomes and overall healthcare quality?

•           What challenges do medical laboratories face in terms of integration with other healthcare services?

•           What are the key factors influencing the efficiency and effectiveness of medical laboratories?

•           How are technological advancements being adopted in medical laboratories, and what is their impact?

The meta-analysis was segmented to evaluate trade-offs among MLS functions, roles, importance, and geography. Results were presented based on the research goals and objectives. [Line 134-206]

5

The authors write: “… underscore the need for this review to identify key technical attributes for public health impact and high-quality services in LMICs”. Giving arguments that there is a need for a review is not the same as that this is the review.

Thanks for your comment.

Please see the full referenced clause in [Line 72-74]

  “Similar issues in sub-Saharan Africa underscore the need for this review to identify key technical attributes for public health impact and high-quality services in LMICs [30]”.

This article is a “Scoping review,” as captured by the Methodology. The scoping review is a type of knowledge synthesis that follows a systematic approach to mapping evidence on a topic and identifying main concepts, theories, sources, and knowledge gaps [57]. However, other types of reviews are rather more extensive and in-depth, such as “Comprehensive” and “Systematic” reviews.

This article reviewed extensively 59 journal articles, identified in the result section, key technical attributes for public health impact and high-quality services in LMICs, [ Line 207-493], documented results in line with the 5 research questions and came up with the conclusion below:

[Line 664-689] “In conclusion, this scoping review underscores the indispensable role of medical laboratories in healthcare delivery. Medical laboratories are pivotal in diagnostics, disease management, and public health initiatives, particularly in LMICs. They facilitate early disease detection, guide clinical decision-making, and support pandemic preparedness. The integration of innovative technologies, especially artificial intelligence, offers significant opportunities to enhance diagnostic accuracy and operational efficiency…...”

S/N

REVIEWER 2 COMMENTS

AUTHORS’ RESPONSES

1

To conduct a review, it is necessary to know the methodology used (e.g. PRISMA), show the databases consulted and why they were selected, the search formulas, the selection criteria, etc. Section 2 should be expanded.

Thank you,

Thanks for the observation.

I have incorporated a well-defined methodology for searching, selecting, and evaluating databases consulted following the PRISMA extension for Scoping Reviews.

Additionally, section 2, on Material and Methodology, has been extensively expanded. Please see [Line 134-206]

2.1 Methodology

The method used for this article is scoping reviews, a type of knowledge synthesis, that follows a systematic approach to map evidence on a topic and identify main concepts, theories, sources, and knowledge gaps. The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) was adopted. The PRISMA-ScR (PRISMA extension for Scoping Reviews) was developed according to published guidance by the EQUATOR (Enhancing the Quality and Transparency of Health Research) Network for the development of reporting guidelines [57].

We evaluated studies for the roles and impact of MLS in public health delivery. The aim was to assess the primary outcomes, strengths, and shortcomings of the collected studies by analyzing selective information based on identified criteria and parameters by evaluating the defined goal and scope of the study, the roles of MLS, the impact in healthcare, and the main limitations, conclusions, and recommendations. Overall, we aimed to evaluate the roles and impact of MLS in healthcare service delivery.

To achieve this aim, this review was organized as follows: The introduction provides a background and context of MLS, particularly in LMICs. The next section presents the methodology used to screen the information from the database and the main areas of the scooping review and meta-analysis. The gathered data from the scooping review was summarized in line with the research questions, goals, and objectives and reported as results. Based on the analyzed information, recommendations and future direction were documented.

2.2 Methodology and strategy for searching and filtering the literature

Databases, keywords, and strategy criteria: The meta-analysis was conducted based on the concept of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology for the assessment and identification of relevant and targeted bibliography when scoping reviews [57]. An initial criterion was followed for source identification based on two main overall concepts: Laboratory medicine and healthcare delivery.

A systematic screening was conducted in the primary databases of the platforms Web of Science and Scopus and other search engines, including ResearchGate and ProQuest, for scientific publications in scientific journals. Furthermore, PubMed websites, consultant group websites, and Google Scholar were accessed for screening, robust checking, and identification of reports unavailable in primary databases. The search keywords were medical laboratory, laboratory medicine, healthcare service delivery, the impact of laboratory science, medical laboratory in LMICs, laboratory medicine, emerging technologies, and artificial intelligence, amongst others.

The main criteria for filtering (exclusion/inclusion) the results were works included in journal publications, publicly available reports, theses, conference proceedings, and class projects. The selected studies' time frames were from January 2001 through November 2024, and they focused on Africa, Asia, North America, and European regions and the English version of published works.

The PRISMA logical model was used for the overall data collection. Initially, using the combinations of keywords resulted in c. 20,000 records from the selected databases. The second screening was done to identify duplicate studies (e.g., conferences and papers), resulting in c. 10,000 records. The third screening involved excluding records on title, abstract, and relevance (e.g., not full report available but a summary, reviews/perspectives articles, studies related only to the product without analysis of the packaging system, and language), resulting in c. 400 studies for eligibility. The final screening, where a more exhaustive set of criteria were applicable, such as meeting the criteria of geographical boundaries and results reported as absolute values rather than in relative %, among others, resulted in the final group of studies for inclusion in the analysis and further processing of the information provided in this work. Studies included and meeting the main criteria led to 58 studies for this meta-analysis.

2.3. Methodology for analyzing the reported information

The selected papers collected and classified according to the PRISMA methodology were analyzed and reviewed in line with the research questions, goals, and objectives.

After all the information was extracted and analyzed, comparison and further cross-analysis among studies and the main analyses for research goals were conducted. The main gaps and recommendations were outlined. Overall, the meta-analysis was focused on answering the following specific questions.

•           What are the primary roles and functions of medical laboratories in the healthcare delivery system?

•           How do medical laboratories impact patient outcomes and overall healthcare quality?

•           What challenges do medical laboratories face in terms of integration with other healthcare services?

•           What are the key factors influencing the efficiency and effectiveness of medical laboratories?

•           How are technological advancements being adopted in medical laboratories, and what is their impact?

The meta-analysis was segmented to evaluate trade-offs among MLS functions, roles, importance, and geography. Results were presented based on the research goals and objectives. [Line 134-206]

2

Table 1: Summarizing the roles of medical laboratories in healthcare delivery, uses only 4 papers. BUT are the title and objective of the study resolved with those 4?

Many thanks for your observation.

Additional papers were added to the original 4 to further resolve the research objectives of the study as it relates to medical laboratories' roles in healthcare delivery [Line 254-255]

Sources: [7,10,21-23,32-34, 36-39,41,44-45, 51]

3

There are NO research questions or objectives or goals of this study. Without these, it is difficult to determine the impact of the Discussion section.

Thank you for your observation.

Research questions, objectives, and goals have been included in the manuscript. Please see [Lines 106-132]

Research Questions:

What are the primary roles and functions of medical laboratories in the healthcare delivery system?

How do medical laboratories impact patient outcomes and overall healthcare quality?

What challenges do medical laboratories face in terms of integration with other healthcare services?

What are the key factors influencing the efficiency and effectiveness of medical laboratories?

How are technological advancements being adopted in medical laboratories, and what is their impact?

Research Objectives:

To identify and categorize the various roles that medical laboratories play in healthcare delivery.

To assess the impact of medical laboratory services on patient outcomes and healthcare quality.\

To explore challenges and barriers to the effective integration of medical laboratories with other healthcare services.

To evaluate the efficiency and effectiveness of resource utilization within medical laboratories.

To examine the adoption and impact of new technologies in medical laboratories.

Research Goals:

Provide a comprehensive overview of the current state of medical laboratories in healthcare delivery.

Highlight the contributions of medical laboratories to patient care and healthcare outcomes.

Identify gaps in knowledge and areas for further research in the field of medical laboratory science.

Propose recommendations for improving the integration, efficiency, and effectiveness of medical laboratory services.

Foster greater awareness and appreciation of the critical role that medical laboratories play in the healthcare system. [Lines 106-132]

4

The authors must present a results section to propose goals.

Thank you for your observations.

The result session has been adjusted to align with the research questions.

To answer research question 1: What are MLs’ primary roles and functions in the healthcare delivery system? MLS is essential for disease screening, diagnosis, prognosis, therapy, and follow-up, in addition to Infectious illness epidemiology and substance ad-diction research. [Line 212-215]

3.3 Laboratory Testing Process to Clinical Decision-Making

This addresses research question 2: How do MLs impact patient outcomes and overall healthcare quality? [Line 331-333]

3.5 The Roles of Artificial Intelligence Technologies in Modern Laboratory Medicines

Adoption of Artificial Intelligence (AI) is a 3-fold approach to resolving research questions 3,4 and 5 [Line 421-423]

5

AI training for all medical and care staff is a current challenge, but this study does not take this into account.

Many thanks for your observation.

Challenges related to AI training and capacity building were earlier mentioned in the article. However, additional text has been introduced to elaborate on the challenges in the manuscript. Please see [Line 449-460]

However, successful integration of AI in laboratory medicine in LMICs requires overcoming several obstacles, including data scarcity, the need for contextually relevant algorithms, ethical considerations, capacity building, and infrastructure improvements. Collaboration and partnerships among stakeholders—such as LMICs, high-income countries, international organizations, and research institutions—are essential for knowledge exchange, resource sharing, and capacity development in the field of AI in laboratory medicine. [23, 33, 35].

Challenges related to capacity building for AI and machine learning (ML) in health care persist. The implementation of ML in comparison with other technologies using the framework of Nonadoption, Abandonment, and Challenges to the Scale-Up, Spread, and Sustainability of Health and Care Technologies (NASSS) remains a “hard nut to be cracked” [58].

Integration of AI: AI in laboratory medicine enhances diagnostic accuracy, streamlines workflows, and supports predictive analytics, leading to tailored treatment plans and improved patient management.

Challenges in LMICs: Laboratories in LMICs face challenges such as inadequate infrastructure, limited access to technology, and insufficient data, hindering effective healthcare delivery and disease management. [Line 649-651]

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Comments on the Quality of English Language

The English wording is confusing, lacks coherence, and lacks a common thread. Syntax and grammar should be reviewed in the full text.

Many thanks for your observations.

The manuscript has been extensively improved with the “Grammarly software app”. The syntax, grammar, and text coherence have been extensively reviewed.

S/N

REVIEWER 1 COMMENTS

AUTHORS’ RESPONSES

1

2. Major Concerns

2.1. Structural and Logical Coherence 2.1.1. Abstract Emphasis

 The abstract refers to "medical laboratories (MLs)", emphasizing their role in public health, while their clinical role is understated.

Many thanks for this observation.

The abstract has been adjusted to emphasize the roles of Medical Laboratories (MLs) in clinical practices.

Medical laboratories are vital in clinical practice, offering essential diagnostic services to identify diseases like infections, genetic disorders, and cancers. They monitor treatment effectiveness by analyzing patient samples, enabling healthcare providers to optimize therapies. Additionally, they support personalized medicine by tailoring treatments based on genetic and molecular data, and ensure test accuracy through strict quality control measures, thereby enhancing patient care. [Lines 13-18]

2

2.1.2. Organization of the Introduction

The introduction covers diverse topics: pandemic preparedness, AI, resource constraints in LMICs, and QMS requirements (ISO 15189:2022). While these are all significant, the narrative flow could be improved by foregrounding a clear knowledge gap and establishing a cohesive rationale for the scoping review early on. Consider defining the problem statements succinctly and showing how they logically lead to the research questions.

Many thanks!

The narrative flow has been improved by foregrounding a clear knowledge gap and establishing a cohesive rationale for the scoping review early on.  The problem statements have been defined succinctly showing how they logically lead to the research questions

The scoping review titled "Medical Laboratories in Healthcare Delivery: A Scoping Review of Their Roles and Impact" addresses several critical issues that hinder the optimization of laboratory services in healthcare. These issues include a lack of comprehensive understanding of the roles of medical laboratories (MLs), inconsistent measurement of their impact, integration challenges with other healthcare services, inefficiencies in resource allocation, the rapid pace of technological advancements, and workforce challenges.

To address these gaps, the review poses the following research questions:

1.         What are MLs’ primary roles and functions in the healthcare delivery system?

2.         How do MLs impact patient outcomes and overall healthcare quality?

3.         What challenges do MLs face regarding integration with other healthcare services?

4.         What are the key factors influencing the efficiency and effectiveness of MLs?

5.         How are technological advancements being adopted in MLs, and what is their impact?

These questions aim to provide a comprehensive understanding of the contributions of MLs to healthcare, assess their impact, identify integration challenges, evaluate resource management, and explore the adoption of new technologies. [Line 114-130]

3

2.1.3. Results–Discussion Alignment The results are thorough, summarizing diverse findings (e.g., diagnostic limitations in LMICs, QMS frameworks, and AI applications). However, the discussion section should be explicitly mapped to these results, drawing out implications for practice or policy. Sometimes, the discussion reads as an extended results summary rather than a reflection on how the findings address the stated research objectives (pp. 4–16). Strengthening this link will clarify the manuscript's contribution to the field

Many thanks for your recommendations.

The discussion session has been strengthened.

The scoping review titled "Medical Laboratories in Healthcare Delivery: A Scoping Review of Their Roles and Impact" provides a comprehensive analysis of the multifaceted roles and impacts of medical laboratories (MLs) in healthcare systems, particularly in LMICs. Here is an extensive discussion of the key findings:

4.1. Roles in Healthcare

4.1.1 Diagnostic Services

Medical laboratories are crucial for providing essential diagnostic services that facilitate early disease detection and monitoring. These services are vital for timely treatment and better patient outcomes. MLs conduct a wide range of tests, including microbiology, hematology, biochemistry, cytopathology, histopathology, immunology, blood banking, molecular biology, and cytotechnology. These tests help in diagnosing infections, genetic disorders, cancers, and other diseases, thereby supporting clinical decision-making and patient management.

4.1.2 Disease Surveillance

MLs play a significant role in disease surveillance by monitoring disease prevalence and outbreaks. This function supports public health initiatives and informs policy decisions. For example, during the COVID-19 pandemic, MLs were instrumental in diagnosing the disease and contributing to early detection efforts. This early detection helped in controlling the spread of the virus and managing the pandemic more effectively.

4.1.3. Clinical Decision Support

MLs assist clinicians in selecting appropriate tests and interpreting results, which enhances the accuracy of diagnoses and treatment plans. By providing precise and reliable test results, MLs support clinicians in making informed decisions about patient care. This collaboration between laboratory professionals and clinicians is essential for optimizing healthcare delivery and improving patient outcomes.

4.1.4 Emergency Response

During pandemics and other health crises, MLs are critical to rapidly responding to health emergencies. For instance, during the COVID-19 pandemic, MLs were pivotal in testing and diagnosing the virus, which helped minimize the spread of the disease. The ability of MLs to quickly adapt and respond to emerging health threats is crucial for effective emergency management.

4.2. Challenges in LMICs

4.2.1 Resource Constraints

One of the major challenges faced by MLs in LMICs is the lack of human, financial, and technical resources. These constraints hinder the effective delivery of healthcare services. Inadequate infrastructure, outdated equipment, and insufficient funding are common issues that limit the capacity of MLs to provide high-quality diagnostic services.

4.2.2 Access to Diagnostics

Restricted access to diagnostic services in LMICs negatively impacts various aspects of healthcare delivery. For example, in many African countries, a significant proportion of HIV-positive individuals do not receive regular viral load monitoring and access to tuberculosis testing is limited. This lack of access to essential diagnostic services leads to missed opportunities for early detection and treatment of diseases, resulting in poorer health outcomes.

4.2.3 Infrastructure and Policy Issues

The absence of standardized protocols for evaluating and registering diagnostic tools further complicates the integration of advanced technologies in LMICs. This lack of standardization results in missed opportunities to address critical healthcare challenges. Additionally, the disconnection between policy formulation, strategic planning, and budgeting leads to fragmented laboratory services and suboptimal operations.

4.3. Technological Advancements

4.3.1 Artificial Intelligence (AI)

The integration of AI in laboratory medicine has the potential to revolutionize diagnostic processes and improve patient outcomes. AI technologies can enhance diagnostic accuracy, streamline workflows, and support predictive analytics. For example, AI can analyze medical images with exceptional accuracy, predict patient outcomes, and guide tailored treatment plans. However, the successful implementation of AI in LMICs requires substantial investments in digital infrastructure, data-sharing networks, and regulatory frameworks.

4.3.2 Point-of-Care Technologies

Innovations such as GeneXpert® and m-Pima® have improved testing specificity and convenience, reducing turnaround times for results and enhancing patient retention. These point-of-care technologies are particularly beneficial in resource-limited settings, where access to centralized laboratory facilities may be limited. By providing rapid and accurate diagnostic results, these technologies support timely clinical decision-making and improve patient management.

4.4. Quality Management Systems (QMS)

Implementing QMS, particularly ISO 15189: 2022, is essential for ensuring the accuracy, reliability, and timeliness of test results. A comprehensive QMS covers all aspects of laboratory operations, including standard operating procedures, equipment calibration and maintenance, internal and external quality control, staff training and competency assessment, documentation and record-keeping, regular audits and reviews, and corrective and preventive actions. By adhering to these quality management measures, MLs can provide high-quality diagnostic services that are crucial for effective patient care and clinical decision-making.

4.5. Collaborative Efforts

Collaboration among governments, healthcare organizations, and laboratory professionals is crucial for strengthening laboratory systems and developing robust infrastructures to meet modern healthcare demands. Effective collaboration can enhance the integration of MLs with other healthcare services, improve resource allocation and utilization, and support the adoption of new technologies. By working together, stakeholders can address the challenges faced by MLs and ensure that they are well-equipped to provide high-quality diagnostic services.

4.6. Future Directions

4.6.1 Investments in Laboratory Infrastructure

Investing in laboratory infrastructure is essential for improving the capacity of MLs to provide high-quality diagnostic services. This includes upgrading equipment, enhancing facilities, and ensuring adequate funding for laboratory operations. Strengthening laboratory infrastructure will enable MLs to better support healthcare delivery and respond to emerging health threats.

4.6.2 Adoption of Innovative Technologies

The adoption of innovative technologies, such as AI and point-of-care diagnostics, can significantly enhance the capabilities of MLs. These technologies can improve diagnostic accuracy, reduce turnaround times, and support personalized medicine. However, their successful implementation requires addressing challenges related to data quality, digital infrastructure, and regulatory frameworks.

4. 6.3 Establishment of Regulatory Frameworks

Establishing robust regulatory frameworks is essential for ensuring the safe and effective use of new technologies in laboratory medicine. These frameworks should address ethical considerations, data privacy, and security, and ensure that AI algorithms are free from bias. By providing clear guidelines and standards, regulatory frameworks can support the responsible integration of new technologies into laboratory practices.

4.6.4 Ongoing Research and Development

Ongoing research and development are crucial. [Lines 654-858]

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2.2. Conceptual Clarity 2.2.1. Roles Versus Processes

 While the manuscript clarifies MLs' roles (diagnostic services, disease surveillance), it occasionally lists processes (e.g., QMS, ISO compliance) alongside roles (Table 1, p. 6–7). Maintaining a distinction between functional roles (e.g., diagnostic services) and enabling processes (e.g., QMS) would improve conceptual sharpness.

Many thanks for the recommendation.

Distinctions were made between functional roles and enabling processes of medical laboratories. The previous pie chart was substituted with another, and an explanation was offered for each process.

 3.2 Laboratory Testing Process to Clinical Decision-Making

Each process in medical laboratories plays a significant role in the healthcare delivery system. By ensuring accuracy and reliability at every step, medical laboratories contribute to better patient care and improved health outcomes.

 The pie chart below illustrates the various processes in medical laboratories:

Figure 2: Pie chart summarizing the various processes in medical laboratories

Sources: [7, 23, 32, 33, 35, 40-42]

Figure 2 represents the key processes involved in medical laboratories and their respective proportions:

•           Sample Collection (15%): This is the initial step where biological samples (e.g., blood, urine, tissue) are collected from patients. Proper sample collection is crucial as it directly impacts the accuracy and reliability of subsequent tests. Accurate sample collection is essential to avoid contamination and ensure the integrity of the samples. Proper techniques and protocols must be followed to obtain reliable results.

•           Laboratory Testing (25%): Once samples are collected, they undergo various diagnostic tests. This process involves analyzing the samples using specialized equipment and techniques to detect diseases, infections, or other medical conditions. This is the core function of medical laboratories. The quality and precision of the tests directly affect the diagnosis and treatment of patients.

•           Data Analysis (20%): After testing, the data generated from the tests are analyzed. Laboratory professionals interpret the results to provide meaningful insights. This step is critical for ensuring the accuracy and validity of the test results. A thorough analysis of test data is necessary to identify any abnormalities or patterns that may indicate a medical condition. This step requires expertise and attention to detail.

•           Result Interpretation (15%): The analyzed data are then interpreted to provide a clear understanding of the patient's condition. This involves comparing the results with reference values and considering the patient's medical history and symptoms. Interpreting the results accurately is crucial for providing actionable information to clinicians. Misinterpretation can lead to incorrect diagnoses and inappropriate treatments.

•           Clinical Decision (15%): Based on the interpreted results, clinicians make informed decisions regarding patient care. This may include diagnosing a condition, determining the severity of a disease, or deciding on the appropriate treatment plan. The decisions made by clinicians based on laboratory results are vital for effective patient care. Accurate and timely information from the laboratory supports better clinical outcomes.

•           Patient Treatment (10%): The final step involves implementing the treatment plan based on the clinical decisions. This may include prescribing medications, recommending lifestyle changes, or scheduling follow-up tests and appointments. Implementing the right treatment plan based on laboratory findings ensures that patients receive appropriate care, leading to improved health outcomes. [Line 328-374]

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2.2.2. Scope of AI Discussion

 The manuscript highlights AI's ability to enhance diagnostic accuracy, streamline workflows, and predict patient outcomes (pp. 11–13). However, parts of the conclusion make broad statements about AI's "transformative capabilities" without enough direct linkage to the specific data presented. Grounding these AI discussions more explicitly in the reviewed literature will underscore the evidence base.

Many thanks for your observation.

The manuscript’s literature has been enhanced to directly link the specific data presented to AI’s “transformative capabilities”.

3.8 Transformative capabilities of Artificial intelligence in Medical Laboratory

The scoping review article highlights the indispensable role of medical laboratories (MLs) in healthcare delivery, particularly in LMICs. It emphasizes the need for investments in laboratory infrastructure, the adoption of innovative technologies, and collaborative efforts to enhance healthcare outcomes. In this context, the transformative capabilities of Artificial Intelligence (AI) can significantly contribute to achieving these goals.

3.8.1 Enhanced Diagnostic Accuracy

AI technologies, particularly machine learning algorithms, have demonstrated exceptional accuracy in analyzing medical images and interpreting complex datasets. For instance, AI can analyze radiographs, MRIs, and other medical images with higher precision than traditional methods, leading to earlier and more accurate diagnoses of conditions like cancers and neurological disorders [1]. This capability directly supports the article's emphasis on the importance of accurate diagnostics in improving patient outcomes and healthcare quality.

3.8.2 Predictive Analytics

AI's predictive analytics capabilities enable healthcare providers to anticipate medical events and patient outcomes with unprecedented accuracy. By analyzing vast datasets, AI can predict patient readmission risks, disease progression, and potential complications [1]. This allows for proactive interventions, which can reduce hospital readmission rates and improve overall patient management. The article highlights the need for effective disease surveillance and early detection, which AI can significantly enhance through predictive analytics.

3.8.3 Personalized Medicine

AI can analyze extensive patient data, including genetic and molecular information, to develop personalized treatment plans tailored to individual needs [2]. This approach improves treatment efficacy and reduces adverse reactions, aligning with the article's call for integrating innovative technologies to enhance patient care. Personalized medicine, supported by AI, ensures that treatments are optimized for each patient, leading to better health outcomes.

3.8.4 Operational Efficiency

AI can automate routine tasks in medical laboratories, such as sample sorting, data entry, and preliminary analysis [2]. This automation frees up laboratory professionals to focus on more complex analyses and decision-making processes. By streamlining workflows, AI reduces turnaround times for test results, which is crucial in emergency situations and for timely clinical decision-making. The article emphasizes the need for efficient laboratory operations, which AI can significantly improve.

3.8.5 Real-Time Monitoring and Intervention

AI extends to real-time patient monitoring, particularly in intensive care and chronic disease management [1]. Advanced AI systems can analyze continuous streams of patient data to predict critical events before they occur, allowing for timely interventions. This capability is vital for managing health crises and ensuring rapid responses, as highlighted in the article's discussion on the role of MLs in emergency response and pandemic preparedness.

3.8.6 Drug Discovery and Development

AI accelerates the drug discovery process by analyzing molecular structures and biological interactions to identify potential drug candidates [1]. This reduces the time and cost associated with bringing new drugs to market. The article calls for ongoing research and development to address evolving healthcare challenges, and AI's role in drug discovery supports this by facilitating the development of new treatments and therapies.

3.8.7 Addressing Challenges in LMICs

AI can help overcome some of the challenges faced by MLs in LMICs, such as limited access to advanced diagnostic tools and insufficient data availability [2]. AI-driven solutions, such as portable diagnostic devices and telemedicine platforms, can extend the reach of healthcare services to remote and underserved areas. By providing cost-effective and scalable diagnostic solutions, AI supports the article's recommendations for improving healthcare delivery in resource-limited settings.

3.8.8 Ethical and Regulatory Considerations

While AI offers transformative capabilities, it also raises ethical and regulatory concerns, such as data privacy, security, and potential biases in algorithms [2]. Establishing robust regulatory frameworks is essential to address these issues and ensure the responsible integration of AI into healthcare systems. The article emphasizes the need for collaborative efforts among stakeholders, including governments and healthcare organizations, to develop and implement these frameworks.

In conclusion, the transformative capabilities of AI align closely with the article's recommendations for enhancing the role of MLs in healthcare delivery. By improving diagnostic accuracy, enabling predictive analytics, supporting personalized medicine, enhancing operational efficiency, and facilitating real-time monitoring, AI can significantly contribute to better health outcomes. Additionally, AI can help address the challenges faced by MLs in LMICs, supporting the article's call for investments in laboratory infrastructure and the adoption of innovative technologies. Collaborative efforts and robust regulatory frameworks are essential to fully realize the potential of AI in transforming healthcare delivery. [Line 579-652]

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2.3. Methodology

2.3.1. Search Strategy and Date Range

The methodology states that studies from 2001 through November 2024 were included, focusing on Africa, Asia, and Europe. While this time frame is understandable, it risks excluding older foundational papers and non-English sources. Justify why earlier studies are less relevant and acknowledge potential selection bias, particularly for LMIC regions where local-language publications might be pivotal

Thanks for the observations.

2.3 Justification for the Time Frame

The decision to include studies from 2001 through November 2024 in the scoping review is based on several key considerations:

The manuscript has been updated with justification for why earlier studies are less relevant and acknowledgment of potential selection bias.

 2.3.1 Relevance of Recent Advances

The field of medical laboratory science has seen rapid advancements in technology, methodologies, and practices over the past two decades. Including studies from 2001 onward ensures that the review captures the most current and relevant developments, which are crucial for understanding the present state and future directions of the field.

2.3.2 Focus on Contemporary Issues

Recent studies are more likely to address contemporary healthcare challenges, such as the COVID-19 pandemic, the integration of artificial intelligence, and the implementation of new diagnostic technologies. These issues are highly relevant to current healthcare delivery and are best understood through recent literature.

2.3.3 Quality and Credibility:

Recent publications are often subject to more rigorous peer review processes and adhere to current standards of research quality and ethics. This enhances the credibility and reliability of the findings included in the review.

2.3.4 Potential Selection Bias

While the chosen time frame is justified, it is important to acknowledge the potential for selection bias, particularly in the context of LMIC regions:

•           Exclusion of Older Foundational Papers: Older foundational papers may contain valuable insights and historical context that could inform current practices and developments. Excluding these papers might result in a loss of important background information and a less comprehensive understanding of the evolution of medical laboratory science.

•           Language Bias: The focus on English-language publications may exclude significant research published in local languages. In LMIC regions, local-language publications can be pivotal in understanding region-specific healthcare challenges and solutions. This exclusion could lead to an incomplete representation of the research landscape and potentially overlook valuable contributions from non-English-speaking researchers.

•           Geographic and Cultural Bias: Research conducted in LMIC regions may be underrepresented if it is not published in high-impact international journals. Local studies, which might be published in regional journals or in languages other than English, provide critical insights into the unique healthcare contexts of these regions. Excluding such studies could result in a biased understanding of the healthcare challenges and innovations in LMICs.

Expanding the search to include regional databases and journals can help capture a wider range of studies, including those that may not be indexed in major international databases. This approach can ensure a more inclusive and representative review.

By acknowledging these potential biases, the scoping review can provide a more balanced and comprehensive understanding of the roles and impacts of medical laboratories in healthcare delivery, particularly in LMIC regions. [Line 211-258]

7

2.3.2. PRISMA-ScR Flow Diagram The manuscript mentions using PRISMA-ScR guidelines and reports an initial screening of ~20,000 records narrowed to 59. Including a detailed PRISMA-ScR flow diagram—showing the steps from identification to final inclusion—would considerably improve transparency.

Many thanks!

The PRISMA-ScR flow diagram showing the steps from identification to final inclusion has been incorporated into the manuscript.

Figure 1: Selection of a source of evidence using the PRISMA-ScR guidelines [Lines 204-208]

8

2.4.QMS and Cost-Effectiveness 2.4.1. Placement of Quality Management Model The discussion of a "cost-effective model for QMS in MLS" is an important practical contribution, yet it appears relatively late (Section 3.4) and is not heavily discussed elsewhere. Strengthening the positioning and rationale for this QMS model, mainly how it addresses the known gaps in LMICs' laboratory frameworks, would add depth to the paper's practical implications.

 Many thanks!

The positioning and rationale for this QMS model, mainly how it addresses the known gaps in LMICs' laboratory framework was further expatiated in the discussion section of the manuscript.

4.6 Strengthening the Positioning and Rationale for the Cost-Effective QMS Model in MLS

The discussion of a "cost-effective model for Quality Management Systems (QMS) in Medical Laboratory Science (MLS)" is indeed a crucial practical contribution. To enhance its impact and relevance, it is essential to strengthen its positioning and rationale, particularly in addressing the known gaps in LMICs' laboratory frameworks. Here’s an expand-ed discussion:

4.6.1 Importance of a Cost-Effective QMS Model

•           Ensuring Accuracy and Reliability: A robust QMS ensures the accuracy, reliability, and timeliness of laboratory test results. This is critical for effective patient care and clinical decision-making. In LMICs, where resources are often limited, a cost-effective QMS can help maintain high standards without imposing significant financial burdens1.

•           Enhancing Laboratory Efficiency: Implementing a QMS streamlines laboratory processes, reduces errors, and improves overall efficiency. This is particularly important in LMICs, where laboratory resources and personnel may be stretched thin. A cost-effective QMS can optimize resource utilization and enhance productivity [2].

•           Supporting Public Health Initiatives: Accurate and reliable laboratory results are essential for disease surveillance, outbreak detection, and public health interventions. A well-implemented QMS supports these functions by ensuring that laboratory data are dependable and actionable [3].

4.6.2 Addressing Gaps in LMICs' Laboratory Frameworks

•           Resource Constraints: LMICs often face significant resource constraints, including limited funding, outdated equipment, and insufficient trained personnel. A cost-effective QMS model addresses these challenges by providing a structured approach to quality management that maximizes the use of available resources4.

•           Infrastructure and Equipment: Many laboratories in LMICs operate with outdated or inadequate infrastructure and equipment. A QMS model emphasizes regular maintenance, calibration, and validation of equipment, ensuring that laboratories can deliver accurate results even with limited resources5.

•           Training and Competency: A key component of a QMS is continuous training and competency assessment of laboratory personnel. This ensures that staff are skilled and knowledgeable about the latest techniques and standards, which is crucial for maintaining high-quality laboratory services in LMICs [6].

•           Standardization and Protocols: The absence of standardized protocols can lead to variability in test results and reduced reliability. A QMS model provides detailed standard operating procedures (SOPs) for all testing processes, ensuring consistency and accuracy across different laboratories.

•           Quality Control and Assurance: Implementing internal and external quality control measures is essential for detecting and correcting errors in real-time. A QMS model includes rigorous quality control protocols, participation in proficiency testing programs, and regular audits to maintain high standards of care.

4.6.3 Practical Implications and Recommendations

•           Adoption of ISO 15189: 2022: The ISO 15189: 2022 standard outlines requirements for quality and competence in medical laboratories. Adopting this standard as part of the QMS model ensures that laboratories meet international benchmarks for quality and reliability.

•           Continuous Improvement: A QMS model should include mechanisms for continuous improvement, such as regular reviews, feedback loops, and corrective actions. This ensures that laboratories can adapt to changing needs and continuously enhance their services.

•           Cost-Effectiveness: The QMS model should be designed to be cost-effective, focusing on maximizing the impact of available resources. This includes prioritizing essential quality management activities, leveraging existing infrastructure, and seeking cost-sharing opportunities with stakeholders.

Strengthening the positioning and rationale for the cost-effective QMS model in MLS is essential for addressing the known gaps in LMICs' laboratory frameworks. By ensuring accuracy, reliability, and efficiency, a well-implemented QMS can significantly enhance the quality of laboratory services, support public health initiatives, and improve patient outcomes. Collaborative efforts and continuous improvement are key to the successful adoption and sustainability of QMS in resource-limited settings. [Lines 777-838]

9

2.5. Policy and Practice Implications 2.5.1. Linking to Clinical and Policy Outcomes

The review documents multiple challenges (such as lack of infrastructural investment, workforce shortages, and limited resource allocation) but could further underscore how these findings translate into specific, actionable policy recommendations. For instance, how might LMIC governments and international agencies implement the proposed QMS frameworks or AI strategies under current budgetary constraints?

Many thanks for this recommendation.

The manuscript now provides specific, actionable policies and recommendations.

Addressing the challenges of implementing QMS and AI strategies in LMICs under budgetary constraints requires a multi-faceted approach. Here are some actionable policy recommendations:

4.4.1 Quality Management Systems (QMS)

•           Adopt Scalable Frameworks: Implement scalable QMS frameworks like ISO 9001, which can be tailored to the specific needs and capacities of LMICs1. This allows for gradual implementation, starting with critical areas and expanding as resources permit.

•           Leverage International Support: Seek technical and financial assistance from international organizations such as the World Health Organization (WHO) and the World Bank. These organizations can provide funding, training, and resources to support QMS implementation2.

•           Public-Private Partnerships: Encourage partnerships between governments and private sector entities to share the costs and benefits of implementing QMS. This can include joint ventures, shared infrastructure, and co-funded training programs.

•           Capacity Building: Invest in training and capacity-building programs to develop local expertise in QMS. This can be achieved through online courses, workshops, and collaboration with international experts.

4.4.2 AI Strategies

•           Focus on High-Impact Areas: Prioritize AI applications that address the most pressing health challenges, such as disease surveillance, diagnostics, and telemedicine. This ensures that limited resources are used effectively.

•           Build Local Expertise: Establish Centers of Excellence for AI in health care within LMICs. These centers can provide training, conduct research, and develop AI solutions tailored to local needs.

•           Collaborate with Tech Companies: Form partnerships with technology companies to leverage their expertise and resources. These collaborations can help develop cost-effective AI solutions and provide access to cutting-edge technology.

•           Ethical and Inclusive Implementation: Ensure that AI strategies are implemented ethically and inclusively. This involves addressing potential biases in AI systems, ensuring data privacy, and involving local communities in the development and deployment of AI solutions.

•           Innovative Financing: Explore innovative financing mechanisms such as blended finance, which combines public and private investment, and results-based financing, where funding is tied to achieving specific outcomes.

By focusing on these strategies, LMIC governments and international agencies can effectively implement QMS frameworks and AI strategies, even under budgetary constraints [Line 721-776]

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2. Minor Concerns 2.1. Terminology The term "laboratory medicines" should be replaced by "laboratory medicine" (lines 28 and 421).

Thank you!

Correction effected through find and replace.

11

2.2. Redundant Subsections There is overlap between the sections on AI's potential, pandemic roles, and collaboration among clinicians and laboratory scientists (pp. 7–13). Condensing repetitive statements may streamline the reading experience.

Many thanks!

The manuscript has been edited and all redundant and overlapping sentences removed between the sections on AI's potential, pandemic roles, and collaboration among clinicians and laboratory scientists.

12

2.3. Referencing Format

While references are generally cited in numeric style, the manuscript also uses bracketed numbering with varied formatting (e.g., "(Ondoa et al., 2020)," "(WHO, 2019)," "(33, 35, 43)"). Ensure consistency across all citations.

Many thanks!

The manuscript has been extensively reviewed to ensure consistency across all citations.

13

2.6. Use of Lists

2.6.1. List of QMS key elements Control charts are a component of internal quality control (IQC).

Many thanks!

Correction effected with the control chart subsumed under the internal quality control.

Internal Quality Control (IQC): Process of determining the relation between the output or response of a measuring instrument and the value of the input. Laboratories analyze control samples alongside patient samples to monitor test performance. This rigor detects any deviations or errors in the testing process in real-time. Control charts are used to monitor the performance of laboratory tests over time. They help detect trends, shifts, or unusual variations in test results. [Line 476-481]

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2.4. Use of Tables and Figures

2.4.1. Table 1 (pp. 6–7) This table helpfully outlines ML roles and their impacts. Ensuring each role is consistently categorized as a function (e.g., diagnosis, surveillance) or a process (e.g., QMS) would add clarity.

Many thanks!

The ML processes have been separated from the roles as indicated above. [Line 328-374]

However, the idea is to split the roles of ML into granular functional units to elaborate the roles under the diverse divisions captured in the left-most column of Table 1.; beyond diagnosis, surveillance, and processes.

15

2.4.2. Figure 1 (p. 7) The pie chart indicates importance percentages but lacks explicit numeric values or a legend explaining how importance was quantified. Including a short caption detailing the data source would improve clarity.

Many thanks for the recommendation.

The pie chart, previously in figure 1 was substituted with another pie chart, now figure 2. It reflects only the processes in the ML. The data source was captured below in Figure 2 and its quantification is explained.

 The pie chart below illustrates the various processes in medical laboratories and theoretical percentages as estimated by [7, 23, 32, 33, 35, 40-42]

Figure 2: Pie chart summarizing the various processes in medical laboratories

Sources: [7, 23, 32, 33, 35, 40-42]

Figure 2 represents the key processes involved in medical laboratories and their respective proportions:

•           Sample Collection (15%): This is the initial step where biological samples (e.g., blood, urine, tissue) are collected from patients. Proper sample collection is crucial as it directly impacts the accuracy and reliability of subsequent tests. Accurate sample collection is essential to avoid contamination and ensure the integrity of the samples. Proper techniques and protocols must be followed to obtain reliable results.

•           Laboratory Testing (25%): Once samples are collected, they undergo various diagnostic tests. This process involves analyzing the samples using specialized equipment and techniques to detect diseases, infections, or other medical conditions. This is the core function of medical laboratories. The quality and precision of the tests directly affect the diagnosis and treatment of patients.

•           Data Analysis (20%): After testing, the data generated from the tests are analyzed. Laboratory professionals interpret the results to provide meaningful insights. This step is critical for ensuring the accuracy and validity of the test results. A thorough analysis of test data is necessary to identify any abnormalities or patterns that may indicate a medical condition. This step requires expertise and attention to detail.

•           Result Interpretation (15%): The analyzed data are then interpreted to provide a clear understanding of the patient's condition. This involves comparing the results with reference values and considering the patient's medical history and symptoms. Interpreting the results accurately is crucial for providing actionable information to clinicians. Misinterpretation can lead to incorrect diagnoses and inappropriate treatments.

•           Clinical Decision (15%): Based on the interpreted results, clinicians make informed decisions regarding patient care. This may include diagnosing a condition, determining the severity of a disease, or deciding on the appropriate treatment plan. The decisions made by clinicians based on laboratory results are vital for effective patient care. Accurate and timely information from the laboratory supports better clinical outcomes.

•           Patient Treatment (10%): The final step involves implementing the treatment plan based on the clinical decisions. This may include prescribing medications, recommending lifestyle changes, or scheduling follow-up tests and appointments. Implementing the right treatment plan based on laboratory findings ensures that patients receive appropriate care, leading to improved health outcomes. [Line 328-374]

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2.5. Flowcharts and Visual Summaries

 The laboratory testing process flowchart (Figure 2, p. 10) is a valuable illustration. Strengthening the visual link between the text and figure (e.g., referencing the figure at relevant points in the discussion) would help readers connect process steps to realworld implications.

Thanks for the recommendation.

Figure 2 is now Figure 3 in the updated manuscript. Figures 2, 3, and Table 1 are referenced at relevant points in the discussion. See [Line 661,674 1075] for examples.

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3. Recommendations for Revision

3.1. Restructure the Introduction 3.1.1.  Delineate the global significance of MLs clearly and define any key concepts upfront.

Thanks for the recommendations.

The introduction section of the manuscript has been completely revised to delineate the global significance of MLs clearly and define the key concepts upfront.

Medical laboratories (MLs) play a critical role in public health by conducting tests on clinical specimens to aid in patient diagnosis. Their importance extends to pandemic preparedness, emergency response, and healthcare delivery, particularly in re-source-constrained countries. This paper emphasizes the significance of MLs in these areas and highlights the need for developing long-term disease surveillance programs in low- and middle-income countries (LMICs) through accessible and affordable diagnostic technologies [1].

In the post-COVID-19 era, well-equipped laboratories are essential for early diagnosis and improved healthcare delivery. Laboratory professionals not only guide clinical colleagues in test selection and result interpretation but also enhance patient outcomes [2]. By integrating innovation and emerging technologies, laboratory medicine contributes to sustainable healthcare. For instance, artificial intelligence (AI) in laboratory medicine demonstrates exceptional accuracy in analyzing medical images and predicting patient outcomes, which is particularly beneficial for disease surveillance and management in LMICs [3].

Medical laboratory science (MLS) involves the analysis of human and animal tissues, fluids, and biological products, as well as the development of diagnostic equipment. This field encompasses various specialties, including Clinical Chemistry, Hematology, Forensic Science, Histopathology, Molecular Biology, Laboratory Management, and Medical Microbiology [4]. Career progression in MLS typically starts as a medical laboratory assistant and can lead to becoming a medical laboratory scientist. Many professionals use an MLS degree to enter other healthcare fields [5].

However, MLS faces challenges in clinical operations and biomedical research, especially in LMICs, due to evolving standards and a lack of regulatory and quality assurance processes [6]. Reforms in laboratory practices, including structural changes and advocacy, can improve healthcare outcomes [7]. Advancements in laboratory science research and policy can also foster global cooperation [8].

MLs are crucial for public health, contributing to sustainable healthcare by integrating innovation and delivering high-quality services. Challenges include reimagining healthcare systems to be safe, efficient, effective, timely, equitable, and patient-centered [9]. Laboratory scientists should embrace roles in clinical leadership to highlight the importance of laboratory tests in patient care, despite recognition and communication challenges [10]. Collaboration between laboratory professionals and clinical colleagues improves decision-making and healthcare delivery [11]. Utilizing longitudinal data and targeted interventions can improve laboratory orders and disease management [12].

This review identifies the critical roles of laboratory medicine in healthcare delivery in LMICs, strengthening healthcare systems through essential diagnostic services for early disease detection, diagnosis, and monitoring [13]. Laboratory data on disease prevalence and outbreaks help public health officials monitor and mitigate health risks, supporting preventive initiatives like vaccination programs [14]. Research in LMICs addresses local healthcare challenges and contributes to global knowledge, reducing disease impact and improving patient outcomes [15].

Challenges in ML in regions like West Africa include inadequate infrastructure, outdated equipment, lack of policy frameworks, slow workforce development, labor protests, and a shortage of qualified professionals [16]. Solutions include increased financial investments, reducing corruption, and addressing wage disparities [17]. Similar issues in sub-Saharan Africa underscore the need for this review to identify key technical attributes for public health impact and high-quality services in LMICs [18]. This research aims to develop a cost-effective model for quality management (QMS) in MLS in low-income countries, contributing to sustainable healthcare systems [19]. [Line 36-85]

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3.1.2. Integrate the multiple problem statements into a unified argument that seamlessly transitions into the research questions. (pp. 2–4).

Many thanks!

The problem statements have been unified into an argument that seamlessly transitions into the research questions

The scoping review titled "Medical Laboratories in Healthcare Delivery: A Scoping Review of Their Roles and Impact" addresses several critical issues that hinder the optimization of laboratory services in healthcare. These issues include a lack of comprehensive understanding of the roles of medical laboratories (MLs), inconsistent measurement of their impact, integration challenges with other healthcare services, inefficiencies in resource allocation, the rapid pace of technological advancements, and workforce challenges.

To address these gaps, the review poses the following research questions:

1.         What are MLs’ primary roles and functions in the healthcare delivery system?

2.         How do MLs impact patient outcomes and overall healthcare quality?

3.         What challenges do MLs face regarding integration with other healthcare services?

4.         What are the key factors influencing the efficiency and effectiveness of MLs?

5.         How are technological advancements being adopted in MLs, and what is their impact?

These questions aim to provide a comprehensive understanding of the contributions of MLs to healthcare, assess their impact, identify integration challenges, evaluate resource management, and explore the adoption of new technologies. [Line 114-130]

19

3.2. Enhance Methodological Transparency 3.2.1. Include a PRISMA-ScR flow diagram detailing how 20,000+ records were narrowed to 59.

Thanks.

Done in response to #7

20

3.2.2. Justify language and date restrictions in more detail, highlighting possible limitations for LMIC-focused research.

Thanks

Done in response to # 6

21

3.3. Link Results, Discussion, and Conclusion 3.3.1.

 Organize results by thematic areas—e.g., pandemic preparedness, AI integration, and QMS—and mirror these themes in the discussion.

Thanks

Done in response to #5

22

3.3.2. In the conclusion, emphasize how each result addresses a specific research question or problem statement.

Thanks for the recommendations

The conclusion of the manuscript now, emphasizes how each result addresses a specific research question or problem statement.

In conclusion, the findings of this scoping review directly address the identified problem statements and corresponding research questions:

•           Knowledge Gap in Laboratory Contributions: The review elucidates the primary roles and functions of medical laboratories (MLs) in the healthcare delivery system, addressing the first research question. This comprehensive understanding helps optimize lab-based diagnostics and treatments critical to patient outcomes.

•           Impact Measurement Challenges: By examining how MLs impact patient outcomes and overall healthcare quality, the review provides clear metrics and evaluation frameworks, addressing the second research question. This enables a more consistent and adequate measurement of MLs' contributions to healthcare.

•           Integration Issues: The review identifies the challenges MLs face regarding integration with other healthcare services, addressing the third research question. Understanding these challenges helps mitigate inefficiencies and delays in diagnostic processes, ultimately improving patient care.

•           Resource Allocation and Utilization: The review explores the key factors influencing the efficiency and effectiveness of MLs, addressing the fourth research question. Insights into resource management can enhance the quality and timeliness of laboratory services, positively impacting healthcare delivery.

•           Technological Advancements and Adoption: By evaluating how technological advancements are being adopted in MLs and their impact, the review addresses the fifth research question. This assessment helps understand the opportunities and challenges posed by new technologies, contributing to improved healthcare outcomes.

•           Workforce Challenges: Although not explicitly listed as a separate research question, the review's findings on the roles, training, workload, and professional development needs of laboratory personnel provide valuable insights into workforce challenges. This understanding is crucial for enhancing the contribution of laboratory staff to healthcare delivery.

Overall, the scoping review provides a comprehensive framework to understand and optimize the roles, impact, and integration of medical laboratories in healthcare, addressing the critical issues identified in the problem statements. [Line 1030-1070]

23

3.4. Strengthen QMS Focus 3.4.1. Integrate the proposed QMS model more deeply, discussing its potential for cost-effectiveness in resource-limited settings.

Thanks

Done. This was addressed in response #8

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3.4.2. Provide examples of how labs in LMICs might realistically adopt ISO 15189:2022 elements (SOPs, CAPA processes, staff training) despite financial barriers.

Thanks for the recommendations.

The manuscript has been revised to include realistic adoption of ISO 15189: 2022 elements despite financial barriers

4.4. Quality Management Systems (QMS)

Implementing QMS, particularly ISO 15189: 2022, is essential for ensuring the accuracy, reliability, and timeliness of test results. A comprehensive QMS covers all aspects of laboratory operations, including standard operating procedures, equipment calibration and maintenance, internal and external quality control, staff training and competency assessment, documentation and record-keeping, regular audits and reviews, and corrective and preventive actions. By adhering to these quality management measures, MLs can provide high-quality diagnostic services that are crucial for effective patient care and clinical decision-making.

Addressing the challenges of implementing QMS and AI strategies in LMICs under budgetary constraints requires a multi-faceted approach. Here are some actionable policy recommendations:

4.4.1 Quality Management Systems (QMS)

•           Adopt Scalable Frameworks: Implement scalable QMS frameworks like ISO 9001, which can be tailored to the specific needs and capacities of LMICs1. This allows for gradual implementation, starting with critical areas and expanding as resources permit.

•           Leverage International Support: Seek technical and financial assistance from international organizations such as the World Health Organization (WHO) and the World Bank. These organizations can provide funding, training, and resources to support QMS implementation2.

•           Public-Private Partnerships: Encourage partnerships between governments and private sector entities to share the costs and benefits of implementing QMS. This can include joint ventures, shared infrastructure, and co-funded training programs.

•           Capacity Building: Invest in training and capacity-building programs to develop local expertise in QMS. This can be achieved through online courses, workshops, and collaboration with international experts. [Line 734-749]

25

3.5. Policy and Practice Relevance 3.5.1. Conclude with clear, evidence-based recommendations, potentially specifying short-term (infrastructure investments, workforce training) and long-term (AI adoption, inter-lab networks) priorities.

Thanks for the recommendation.

The conclusion section of the manuscript has been reinforced with short and long-term recommendations.

To effectively harness the potential of AI, here are some clear, evidence-based recommendations:

Short-Term Priorities

1.         Infrastructure Investments:

•           Data Centers and High-Performance Computing: Investing in advanced data centers and GPU clusters is crucial to support AI tasks12. Companies like Google and Microsoft are already leading the way with their cloud platforms.

•           AI-Ready Systems: Building AI-ready systems that integrate robotics, AI, and IoT can significantly enhance lab efficiency and productivity3.

2.         Workforce Training:

•           AI Skills Development: Training programs should focus on both technical AI skills and human-centric skills like critical thinking and problem-solving. Organizations like Jobs for the Future (JFF) provide toolkits to support this transition.

•           Upskilling and Reskilling: Continuous learning opportunities for employees to adapt to AI technologies are essential. This includes partnerships with educational institutions and online learning platforms.

Long-Term Priorities

1.         AI Adoption:

•           Strategic AI Roadmap: Develop a clear AI strategy that aligns with business objectives. This includes investing in data management, building AI talent, and piloting AI projects before scaling them6.

•           AI Maturity Levels: Progress through AI maturity levels, from awareness to transformational stages, to fully integrate AI into business processes6.

2.         Inter-Lab Networks:

•           Collaborative Research Networks: Establishing inter-lab networks can foster collaboration and innovation. These networks can leverage AI to optimize research environments and improve scientific output37.

•           Smart Lab Connectivity: Integrating AI and IoT in labs to create smart, connected environments that streamline workflows and enhance reproducibility.

By focusing on these short-term and long-term priorities, organizations can effectively navigate the AI landscape and unlock its full potential. [Line 1088-1127]

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3.5.2. Consider summarizing "Key Action Points" for policymakers, funders, and laboratory managers.

Many thanks!

The conclusion section of the manuscript now includes the following key action points:

Here are some key Action Points for policymakers, funders, and managers:

For Policymakers:

•           Infrastructure Funding: Allocate resources for advanced data centers and AI-ready systems.

•           Education and Training: Support AI skills development programs and continuous learning initiatives.

For Funders:

•           Strategic Investments: Invest in AI projects with clear roadmaps and potential for scalability.

•           Collaborative Networks: Fund initiatives that promote inter-lab collaboration and smart lab connectivity.

For Laboratory Managers:

•           AI Integration: Develop and implement AI strategies aligned with lab objectives.

•           Workforce Development: Prioritize upskilling and reskilling of staff to adapt to AI technologies.

These action points will help drive effective AI adoption and maximize its benefits. [Line 1129-1149]

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4. Overall Assessment The manuscript deals with an important and timely subject—medical laboratories' crucial roles and impacts in healthcare delivery, especially in LMICs. The strengths of the paper lie in its extensive literature coverage, the practical emphasis on QMS, and its discussion of emerging technologies like AI. However, the paper would benefit from more evident organization, substantial methodological transparency, and a tighter, more direct mapping of results to conclusions. With revisions to structure, consistent terminology, and more explicit policy implications, this review can offer helpful insights to academic audiences, policymakers, and healthcare practitioners

Many thanks for the comments and invaluable recommendations.

S/N

REVIEWER 1 COMMENTS

AUTHORS’ RESPONSES

1

2.Major Concerns 2.1. Repetition of Content

The manuscript contains significant redundancy, particularly in its discussions on the roles of MLs in diagnostics, disease surveillance, and clinical decision support. These topics appear in multiple sections - particularly in the "Results" and "Discussion" - without introducing additional insights. This unnecessary repetition inflates the length of the manuscript and obscures key findings. A more concise and focused approach would enhance readability and clarity.

Many thanks for your observations.

The manuscript has been extensively revised. The redundant sections have been removed, and the manuscript is now more concise and focused, enhancing readability and clarity.

2

2.2. Lack of Coherence and Structural Clarity

 The manuscript lacks a structured framework, making it difficult for readers to follow the central themes. Thematic consolidation is needed to create a logically flowing discussion.

Many thanks for your observation.

The manuscript has been re-structured along with thematic objectives, hence providing a logical flow for readers.

3

3.1. Introduction and Background:

The introduction sets a broad context but would gain clarity if the scope were more tightly defined. A succinct outline of the paper's objectives would help reduce later redundancies.

 Thanks!

The introduction has been revised to provide clarity and a succinct outline of the paper’s objectives.

Medical laboratories (MLs) play a critical role in public health by conducting tests on clinical specimens to aid in patient diagnosis. Their importance extends to pandemic preparedness, emergency response, and healthcare delivery, particularly in re-source-constrained countries. This paper emphasizes the significance of MLs in these areas and highlights the need for developing long-term disease surveillance programs in low- and middle-income countries (LMICs) through accessible and affordable diagnostic technologies [1]. In the post-COVID-19 era, well-equipped laboratories are essential for early diagnosis and improved healthcare delivery. Laboratory professionals not only guide clinical colleagues in test selection and result interpretation but also enhance patient outcomes [2]. By integrating innovation and emerging technologies, laboratory medicine contributes to sustainable healthcare. For instance, artificial intelligence (AI) in laboratory medicine demonstrates exceptional accuracy in analyzing medical images and predicting patient outcomes, which is particularly beneficial for disease surveillance and management in LMICs [3].

Objectives of the Paper include amongst others:

1.         Identify and Categorize Roles: To identify and categorize the various roles that MLs play in healthcare delivery.

2.         Assess Impact on Patient Outcomes: To assess the impact of medical laboratory services on patient outcomes and healthcare quality.

3.         Explore Integration Challenges: To explore challenges and barriers to effectively integrating MLs with other healthcare services.

4.         Evaluate Resource Utilization: To evaluate the efficiency and effectiveness of resource utilization within MLs.

5.         Examine Technological Adoption: To examine the adoption and impact of new technologies in MLS. [Line 36-61]

4

3.2. Methodology:

While the use of PRISMA-ScR guidelines is commendable, the description of the literature screening process is verbose. Condensing this section would allow greater focus on the review's findings and their implications

Many thanks!

The Methodology has been condensed.

2.1 Methodology

The method used for this article is a scoping review, which systematically maps evidence on a topic to identify main concepts, theories, sources, and knowledge gaps. The PRIS-MA-ScR guidelines were adopted and developed by the EQUATOR Network for reporting guidelines [1]. The review evaluated studies on the roles and impact of medical laboratory science (MLS) in public health delivery, assessing primary outcomes, strengths, and shortcomings. The aim was to evaluate the roles and impact of MLS in healthcare service delivery.

The review is organized as follows: The introduction provides background and context of MLS, particularly in LMICs. The methodology section details the screening of information from databases and the main areas of the scoping review and meta-analysis. The results are summarized in line with the research questions, goals, and objectives. Finally, recommendations and future directions are documented based on the analyzed information.

2.2 Methodology and strategy for searching and filtering the literature

The meta-analysis followed the PRISMA methodology to identify relevant bibliographies for scoping reviews [57]. The focus was on laboratory medicine and healthcare delivery. Systematic screening was conducted using databases like Web of Science, Scopus, ResearchGate, ProQuest, PubMed, and Google Scholar. Keywords included medical laboratory, laboratory medicine, healthcare service delivery, impact of laboratory science, medical laboratory in LMICs, emerging technologies, and artificial intelligence.

The criteria for filtering results included journal publications, publicly available reports, theses, conference proceedings, and class projects, focusing on studies from January 2001 to November 2024 in Africa, Asia, North America, and Europe, and in English. The initial search yielded approximately 20,000 records, which were screened down to 59 studies after removing duplicates and irrelevant records. [Line 120-146]

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3.3. Results and Discussion:

 The discussion repeats findings already presented in the results section without sufficient differentiation. A focused synthesis that avoids duplication is necessary.

Many thanks!

Duplications in the result and discussion have been addressed, and the manuscript is now well-focused.

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4.Specific Comments

 4.1. Sections 3.5 and 3.7 should be merged into one section about AI in laboratory medicine, avoiding unnecessary repetitions.

Thanks.

Sections 3.5 and 3.7 has been summarized and merged into one section. Section 3.7 eliminated and fused to 3.5 as 3.5.1

3.5 The Roles of Artificial Intelligence Technologies in Modern Laboratory Medicine

Adoption of Artificial Intelligence (AI) addresses research questions 3, 4, and 5. AI exhibits exceptional accuracy in analyzing medical images and predicting patient out-comes using extensive datasets [23, 33]. Its importance in tackling healthcare issues, especially in LMICs, has transformed MLS practice. Studies highlight AI's significant impact on laboratory medicine in LMICs, identifying challenges like limited data availability, in-adequate digital infrastructure, and ethical concerns [46, 23, 33].

Effective AI implementation requires substantial investments in digital infrastructure, data-sharing networks, and regulatory frameworks. Collaborative efforts among international organizations, government agencies, and NGOs are recommended to address these challenges and ensure responsible AI integration [23, 33, 35].

AI advancements are crucial for developing countries, enhancing clinical decision-making, improving diagnostic accuracy, and streamlining processes. AI can reduce healthcare disparities, improve patient outcomes, and support disease surveillance [33, 23]. However, successful AI integration in LMICs requires overcoming obstacles like data scarcity, ethical considerations, capacity building, and infrastructure improvements. Collaboration among LMICs, high-income countries, international organizations, and re-search institutions is essential for knowledge exchange and capacity development [23, 33, 35].

3.5.1 Transformative Capabilities of Artificial Intelligence in Medical Laboratories

The scoping review highlights the critical role of medical laboratories (MLs) in healthcare delivery, especially in low- and middle-income countries (LMICs). It emphasizes the need for investments in laboratory infrastructure, innovative technologies, and collaborative efforts to enhance healthcare outcomes. Artificial Intelligence (AI) can significantly contribute to these goals.

•           Enhanced Diagnostic Accuracy: AI technologies, particularly machine learning algorithms, demonstrate exceptional accuracy in analyzing medical images and interpreting complex datasets, leading to earlier and more accurate diagnoses of conditions like cancers and neurological disorders [1].

•           Predictive Analytics: AI's predictive analytics capabilities enable healthcare providers to anticipate medical events and patient outcomes with unprecedented accuracy, allowing for proactive interventions and improved patient management [1].

•           Personalized Medicine: AI can analyze extensive patient data to develop personalized treatment plans tailored to individual needs, improving treatment efficacy and reducing adverse reactions [2].

•           Operational Efficiency: AI can automate routine tasks in medical laboratories, such as sample sorting and data entry, freeing up professionals to focus on more complex analyses and decision-making processes, thus improving operational efficiency [2].

•           Real-Time Monitoring and Intervention: AI extends to real-time patient monitoring, particularly in intensive care and chronic disease management, predicting critical events before they occur and allowing for timely interventions [1].

•           Drug Discovery and Development: AI accelerates the drug discovery process by analyzing molecular structures and biological interactions, reducing the time and cost associated with bringing new drugs to market [1].

•           Addressing Challenges in LMICs: AI-driven solutions, such as portable diagnostic devices and telemedicine platforms, can extend healthcare services to remote and underserved areas, providing cost-effective and scalable diagnostic solutions [2].

•           Ethical and Regulatory Considerations: AI raises ethical and regulatory concerns, such as data privacy, security, and potential biases in algorithms. Establishing robust regulatory frameworks is essential to address these issues and ensure responsible AI integration into healthcare systems [2].

In conclusion, AI aligns closely with the article's recommendations for enhancing the role of MLs in healthcare delivery. By improving diagnostic accuracy, enabling predictive analytics, supporting personalized medicine, enhancing operational efficiency, and facilitating real-time monitoring, AI can significantly contribute to better health outcomes. Additionally, AI can help address challenges faced by MLs in LMICs, supporting the call for investments in laboratory infrastructure and innovative technologies. Collaborative efforts and robust regulatory frameworks are essential to fully realize AI's potential in transforming healthcare delivery. [Line 454-517]

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4.2. Subsections 3.7.6 about "Drug Discovery and Development", 5.5.4, and 5.5.8 should be eliminated, as they do not align well with the manuscript's primary focus.

Thanks.

Subsections 3.7.6, 5.5.4, and 5.5.8 have been eliminated.

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4.3. Lines 15 and 665: "infections, genetic disorders, and cancers" might be replaced by "infections, metabolic disorders, and malignancies."

Thanks

Replacement done.

‘...like infections, metabolic disorders, and malignancies.” [Line 15]

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4.4. The same statement is repeated in lines 46-47 and 63-64

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General revision has taken care of this.

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4.5. Lines 175-176: The sentence ending with "when scoping reviews" is incomplete.

Thanks.

Sentence has been revised.

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4.6. Line 280: The expression "several pieces of literature" is unclear and requires clarification.

Thanks.

Sentence has been rephrased for clarification.

The scoping review of several of literatures yielded results that support the goals, objectives, and research questions identified at the introduction session; these are presented below under different subheadings. [Line 224-226]

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4.7. Lines 328 – 374: The meaning of the percentages used in this section should be clarified.

Thanks.

The meaning of the percentages has been clarified in the manuscript.

The percentages in the pie chart represent the estimated proportion of time and effort allocated to each step in the medical laboratory process. These estimates are based on expert opinions, literature reviews, and practical observations of laboratory workflows. These percentages are derived from a combination of empirical data and expert judgment to provide a comprehensive view of the laboratory process. [Line 283-287]

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4.8. Lines 351 – 362: The distinction between "Data Analysis" and "Result(s) Interpretation" is unclear, leading to conceptual overlap.

Thanks.

The overlap has been removed and further clarification of the distinctions provided.

•           Data Analysis (20%): After testing, the data generated from the tests are analyzed. This includes statistical calculations, identifying patterns, trends, and anomalies, and ensuring data accuracy and validity. This produces raw analytical results, such as numerical values, graphs, and charts, which need further interpretation to be meaningful. This step is critical for ensuring the accuracy and validity of the test results. A thorough analysis of test data is necessary to identify any abnormalities or patterns that may indicate a medical condition. This step requires expertise and attention to detail.

•           Result Interpretation (15%): The analyzed data are then interpreted to provide a clear understanding of the patient's condition. This involves comparing the results with reference values and considering the patient's medical history and symptoms. This guides clinicians in diagnosing conditions, determining treatment plans, and making informed medical decisions. Interpreting the results accurately is crucial for providing actionable information to clinicians. Misinterpretation can lead to incorrect diagnoses and inappropriate treatments. [Line 299-313]

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4.9. Line 379: The axes of Figure 3 should be explained,

Thanks.

The axes are now explained in the manuscript.

Horizontal Axis (X-axis): This axis typically represents the sequential steps or stages in the laboratory testing process. It would show the progression from sample collection, laboratory testing, data analysis, result interpretation, and clinical decision-making, to patient treatment.

Vertical Axis (Y-axis): This axis represents the proportion of time, effort, or resources allocated to each step in the process. It could also indicate the relative importance or impact of each stage on the overall clinical decision-making process.

Together, these axes provide a visual representation of how each step in the laboratory testing process contributes to clinical decision-making and patient care. [Line 336-344]

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4.10. Line 206, Figure 1: The phrase "Records after duplicates removed (n = 1000)" appears incorrect and should be revised.

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Phrase has been revised. [Line 150-151]

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4.11. Line 256: "can provide" should be replaced with "could provide".

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This is done.

“…. the scoping review could provide. [Line 200]

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4.12. Line 664:

The expression "an illustrated cytotechnology" is meaningless.

Thanks

The expression has been revised.

MLs conduct a wide range of tests, including microbiology, hematology, biochemistry, cytopathology, histopathology, immunology, blood banking, and molecular biology. These tests help in diagnosing infections, metabolic disorders, malignancies, and other diseases, thereby supporting clinical decision-making and patient management. [Line 575-580]

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4.13. Lines 424 and 715:

The product names "GeneXpert® and m-Pima® should be replaced with generic names.

Thanks

The product name has been replaced with a generic name.

Innovations such as generic molecular diagnostic devices have… [Line 629]

…. such as generic molecular diagnostic devices…. [Line 338-339]

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4.14. Sections 4.4 and 4.6 should be combined into a single section.

4.4. Quality Management Systems (QMS)

Implementing QMS, particularly ISO 15189: 2022, is essential for ensuring the accuracy, reliability, and timeliness of test results. A comprehensive QMS covers all aspects of laboratory operations, including standard operating procedures, equipment calibration and maintenance, internal and external quality control, staff training and competency assessment, documentation and record-keeping, regular audits and reviews, and corrective and preventive actions. By adhering to these quality management measures, MLs can provide high-quality diagnostic services that are crucial for effective patient care and clinical decision-making.

Addressing the challenges of implementing QMS and AI strategies in LMICs under budgetary constraints requires a multi-faceted approach. Here are some actionable policy recommendations:

4.4.1 Quality Management Systems (QMS)

•           Adopt Scalable Frameworks: Implement scalable QMS frameworks like ISO 9001, which can be tailored to the specific needs and capacities of LMICs1. This allows for gradual implementation, starting with critical areas and expanding as re-sources permit.

•           Leverage International Support: Seek technical and financial assistance from inter-national organizations such as the World Health Organization (WHO) and the World Bank. These organizations can provide funding, training, and resources to support QMS implementation2.

•           Public-Private Partnerships: Encourage partnerships between governments and private sector entities to share the costs and benefits of implementing QMS. This can include joint ventures, shared infrastructure, and co-funded training pro-grams.

•           Capacity Building: Invest in training and capacity-building programs to develop local expertise in QMS. This can be achieved through online courses, workshops, and collaboration with international experts.

4.4.2 Strengthening the Positioning and Rationale for the Cost-Effective QMS Model in MLS

The discussion emphasizes the importance of a cost-effective Quality Management System (QMS) in Medical Laboratory Science (MLS), particularly in LMICs. A robust QMS ensures the accuracy, reliability, and timeliness of laboratory test results, which is critical for effective patient care and clinical decision-making. In resource-limited settings, a cost-effective QMS can maintain high standards without significant financial burdens, streamline laboratory processes, reduce errors, and improve overall efficiency [1, 2, 3].

Addressing gaps in LMICs' laboratory frameworks involves tackling resource constraints, outdated infrastructure, and insufficient trained personnel. A QMS model provides a structured approach to quality management, emphasizing regular maintenance, calibration, and validation of equipment, continuous training and competency assessment of personnel, and the implementation of standardized protocols and rigorous quality control measures [4, 5, 6].

Practical recommendations include adopting the ISO 15189: 2022 standard, which out-lines requirements for quality and competence in medical laboratories and incorporating mechanisms for continuous improvement. The QMS model should be designed to be cost-effective, maximizing the impact of available resources and seeking cost-sharing opportunities with stakeholders.

Strengthening the positioning and rationale for the cost-effective QMS model in MLS is essential for enhancing the quality of laboratory services, supporting public health initiatives, and improving patient outcomes. Collaborative efforts and continuous improvement are key to the successful adoption and sustainability of QMS in resource-limited settings. [Line 635-685]

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4.15. Subsection 4.4.2 should be presented as a separate section.

Thanks

Subsection 4.4.2 is now presented as a separate section (4.6)

4.6 AI Strategies

•           Focus on High-Impact Areas: Prioritize AI applications that address the most pressing health challenges, such as disease surveillance, diagnostics, and telemedicine. This ensures that limited resources are used effectively.

•           Build Local Expertise: Establish Centers of Excellence for AI in health care within LMICs. These centers can provide training, conduct research, and develop AI solutions tailored to local needs.

•           Collaborate with Tech Companies: Form partnerships with technology companies to leverage their expertise and resources. These collaborations can help develop cost-effective AI solutions and provide access to cutting-edge technology.

•           Ethical and Inclusive Implementation: Ensure that AI strategies are implemented ethically and inclusively. This involves addressing potential biases in AI systems, ensuring data privacy, and involving local communities in the development and deployment of AI solutions.

•           Innovative Financing: Explore innovative financing mechanisms such as blended finance, which combines public and private investment, and results-based financing, where funding is tied to achieving specific outcomes.

By focusing on these strategies, LMIC governments and international agencies can effectively implement QMS frameworks and AI strategies, even under budgetary constraints. [Line 694-712]

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4.16. Lines 802, 806, 1094, 1098, 1116, 1121: Several references need correction

Thanks

Corrections effected.

The discussion emphasizes the importance of a cost-effective Quality Management System (QMS) in Medical Laboratory Science (MLS), particularly in LMICs. A robust QMS ensures the accuracy, reliability, and timeliness of laboratory test results, which is critical for effective patient care and clinical decision-making. In resource-limited settings, a cost-effective QMS can maintain high standards without significant financial burdens, streamline laboratory processes, reduce errors, and improve overall efficiency [1, 2, 3].

Addressing gaps in LMICs' laboratory frameworks involves tackling resource constraints, outdated infrastructure, and insufficiently trained personnel. A QMS model provides a structured approach to quality management, emphasizing regular maintenance, calibration, and validation of equipment, continuous training and competency assessment of personnel, and the implementation of standardized protocols and rigorous quality control measures [4, 5, 6].

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4.17. The word "crucial" appears excessively (24 times) and should be used more selectively.

Thanks

This has been addressed.

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5. Conclusion While the revised manuscript addresses an important topic, its impact is diminished due to a fragmented structure and extensive repetition. The paper does not read as a structured scoping review but rather as a compilation of concepts. Substantial revisions are necessary to enhance coherence, eliminate redundancies, and improve logical flow. Careful restructuring will significantly improve the scholarly value of the paper

Many thanks!

The manuscript has been substantially revised as necessary to enhance coherence, eliminate redundancies, and improve logical flow.