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

Abstract

Medical laboratories (MLs) are vital in global healthcare delivery, enhancing diagnostic accuracy and supporting clinical decision-making. This systematic review examines the multifaceted contributions of ML, emphasizing their importance in pandemic preparedness, disease surveillance, and the integration of innovative technologies such as artificial intelligence (AI). Medical laboratories are equally crucial to clinical practices, offering essential diagnostic services to identify diseases like infections, metabolic disorders, and malignancies. 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. The methodology for this systematic review follows the PRISMA-ScR guidelines to systematically map evidence and identify key concepts, theories, sources, and knowledge gaps related to the roles and impact of MLs in public health delivery. This review involved systematic searching and filtering of literature from various databases, focusing on studies from 2010 to 2024, primarily in Africa, Asia, and Europe. The selected studies were analyzed to assess their outcomes, strengths, and limitations regarding MLS roles, impacts, and integration within healthcare systems. The goal was to provide comprehensive insights and recommendations based on the gathered data. The article highlights the challenges that laboratories face, especially in low- and middle-income countries (LMICs), where resource constraints hinder effective healthcare delivery. It discusses the potential of AI to improve diagnostic processes and patient outcomes while addressing ethical and infrastructural challenges. This review underscores the necessity for collaborative efforts among stakeholders to enhance laboratory services, ensuring that they are accessible, efficient, and capable of meeting the evolving demands of healthcare systems. Overall, the findings advocate for strengthened laboratory infrastructures and the adoption of advanced technologies to improve health outcomes globally.

1. Introduction

Medical laboratories (MLs) play a critical role in healthcare by conducting tests on clinical specimens to aid in patient diagnosis, prognosis, and monitoring. Their importance extends to pandemic preparedness, emergency response, and healthcare delivery, particularly in resource-constrained countries. This paper emphasizes the significance of MLs in these areas. It 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].

The objectives of this paper include, amongst others, the following:

• Identify and categorize roles: identify and categorize the various roles that MLs play in healthcare delivery.
• Assess impact on patient outcomes: assess the impact of medical laboratory services on patient outcomes and healthcare quality.
• Explore integration challenges: explore challenges and barriers to effectively integrating MLs with other healthcare services.
• Evaluate resource utilization: evaluate the efficiency and effectiveness of resource utilization within MLs.
• Examine technological adoption: examine the adoption and impact of new technologies in MLS.

These objectives 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.

The following problem statements necessitated this systematic review titled “Medical Laboratories in Healthcare Delivery: A Systematic Review of Their Roles and Impact”:

• Knowledge gap in laboratory contributions: There is a significant lack of comprehensive understanding regarding the full scope of MLs’ roles in healthcare delivery. This gap hinders the ability to optimize lab-based diagnostics and treatments critical to patient outcomes.
• Impact measurement challenges: The impact of ML on healthcare delivery is not consistently or adequately measured. Without clear metrics and evaluation frameworks, assessing how effectively labs contribute to patient care and health system efficiency is difficult.
• Integration issues: MLs often face challenges 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: MLs need to investigate how resources are allocated and utilized. 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 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.

This systematic review titled “Medical Laboratories in Healthcare Delivery: A Systematic 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, this review poses the following research questions:

• What are MLs’ primary roles and functions in the healthcare delivery system?
• How do MLs impact patient outcomes and overall healthcare quality?
• What challenges do MLs face regarding integration with other healthcare services?
• What are the key factors influencing the efficiency and effectiveness of MLs?
• 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.

The research objectives have the following goals:

• Provide a comprehensive overview of the current state of MLs in healthcare delivery.
• Highlight the contributions of MLs to patient care and healthcare outcomes.
• Identify gaps in knowledge and areas for further research in ML science.
• Propose recommendations for improving medical laboratory services’ integration, efficiency, and effectiveness.
• Foster greater awareness and appreciation of the critical role that MLs play in the healthcare system.

2. Materials and Methods

2.1. Methodology

The method used for this article is a systematic review, which systematically maps evidence on a topic to identify the main concepts, theories, sources, and knowledge gaps. The PRISMA-ScR guidelines were adopted and developed by the EQUATOR Network for reporting guidelines [1]. This 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.

This review is organized as follows: The Introduction provides the background and context of MLS, particularly in LMICs. Section 2 details the screening of information from databases and the main areas of this systematic 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 systematic reviews [4]. 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, written in English. The initial search yielded approximately 20,000 records, which were screened down to 58 studies after removing duplicates and irrelevant records.

Figure 1 shows the flow diagram from identification to final inclusion using the PRISMA-ScR guidelines and reports an initial screening of ~20,000 records narrowed to 58.

2.3. Justification for the Time Frame

The decision to include studies from January 2001 to November 2024 in this systematic review is based on several key considerations:

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 onwards ensures that this review captures the most current and relevant developments, which are crucial for understanding the field’s present state and future directions.

2.3.2. Focus on Contemporary Issues

Recent studies are more likely to address contemporary healthcare challenges, such as the COVID-19 pandemic, artificial intelligence integration, and new diagnostic technologies. These issues are highly relevant to current healthcare delivery and are best understood through the 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 this 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 under-represented 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 to 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, this systematic review could provide a more balanced and comprehensive understanding of the roles and impacts of medical laboratories in healthcare delivery, particularly in LMIC regions.

2.4. 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 extracting and analyzing all of the information, we conducted comparisons and further cross-analyses among studies and the primary analyses for research goals. We then outlined the main gaps and offered our recommendations. Overall, the meta-analyses focused on answering the research questions presented in Section 1.

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.

3. Results

This systematic review of several pieces of literature yielded results that support the goals, objectives, and research questions identified in Section 1; these are presented below under different subheadings.

3.1. Medical Laboratories’ Roles in Pandemics

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, follow-up, and infectious illness epidemiology, as well as substance addiction research. Their importance in biomedical research is highly essential. Microbiology, hematology, biochemistry, cytopathology, histopathology, immunology, blood banking, molecular biology, and cytotechnology are some specialist laboratory departments that carry out these functions [5]. This systematic review thoroughly summarizes the crucial function of MLS in the varied and ever-evolving healthcare industry.

Medical laboratory tests are vital throughout many clinical pathways. Etiological diagnosis, patient monitoring, and epidemiologic surveillance are at least three major areas where in vitro diagnostics can contribute to diagnostic reasoning and patient management [5]. Lippi and Plebani (2020) and Pambuccian (2020) [6,7] noted that MLs played a pivotal role in diagnosing COVID-19, significantly contributing to the early detection efforts during the 2019 pandemic. Coronavirus disease 2019 (COVID-19) was a global pandemic instigated by the SARS-CoV-2 virus. Medical laboratory interventions for early detection prevented the rapid escalation of the infection, and the severity of the associated clinical manifestations was eased [5,6,8]. COVID-19 emerged as a significant biological threat, highlighting its insidious nature globally. A pressing issue among scientists and healthcare professionals is the potential role of laboratory medicine in effectively addressing future viral outbreaks [5]. Healthcare challenges are better managed with substantial investments in traditional laboratory resources, strengthening regional networks of clinical laboratories, deploying mobile laboratories, and proactively developing laboratory emergency plans [5,8].

Similarly, Pabbaraju and others (2020) observed that the persistent challenge posed by emerging infectious agents underscores the critical function of public health laboratories in preparing for pandemics. They emphasized fostering strong collaborations among public health laboratories nationwide to develop and validate testing protocols by exchanging knowledge and resources. Furthermore, establishing a unified health authority and laboratory system and robust inter-laboratory cooperation will enhance the robust diagnosis of diseases and reduce medical excursions. The key requirements for efficient MLs include rapid clinical validation capabilities, effective supply chain management, and the proactive creation of multiple supply chains for laboratory reagents and consumables [5,6,8].

The effects of SARS-CoV, the 2009 H1N1 influenza pandemic, MERS-CoV, Ebola, Zika, and most recently, COVID-19, have been long-endured. With this in mind, the next novel or emerging viral outbreak is likely just around the corner. Therefore, a general framework to guide our response to outbreaks of global health concern is needed. This should involve the expertise and direction of government agencies (e.g., FDA/CDC), state and local public health departments, industry partners, clinical laboratories, and healthcare providers [7,8].

Table 1. Summarizing the roles of medical laboratories in healthcare delivery.

Role of Medical Laboratories	Description	Impact on Healthcare	Sources
Diagnostic services	Provide essential tests for disease detection and monitoring.	Early diagnosis leads to timely treatment and better patient outcomes.	[5,8,9,10,11,12,13,14]
Disease surveillance	Monitor disease prevalence and outbreaks.	Supports public health initiatives and informs policy decisions.	[6,10,15,16,17]
Clinical decision support	Assist clinicians in selecting appropriate tests and interpreting results.	Enhances the accuracy of diagnoses and treatment plans.	[9,17,18,19,20]
Research and development	Contribute to clinical trials and the development of new diagnostic technologies.	Drives innovation in healthcare and improves diagnostic capabilities.	[10,12,15,16,18]
Emergency response	Play a critical role during pandemics (e.g., COVID-19 testing).	Facilitates rapid response to health crises, minimizing disease spread.	[9,14,17,19,20,21]
Quality assurance	Ensure the reliability and accuracy of laboratory tests.	Maintains high standards of care and patient safety.	[13,15,16,19,22]
Education and training	Train healthcare professionals in laboratory practices and test interpretation.	Builds a knowledgeable workforce that improves healthcare delivery.	[8,13,15,18,21]
Substance abuse testing	Conduct tests to detect drug use and monitor rehabilitation.	Supports treatment programs and public safety initiatives.	[9,10,12,16,18]
Genetic testing	Analyze genetic material to identify hereditary conditions.	Aids in personalized medicine and risk assessment for diseases.	[12,13,14,15,17]
Transfusion services	Ensure safe blood transfusions through compatibility testing.	Reduces the risk of transfusion reactions and improves patient outcomes.	[10,12,16,21,23]

Sources: [5,6,7,8,9,10,12,13,14,15,16,17,18,19,20,21,22,23].

highlights the multifaceted roles of MLs and their significant contributions to healthcare systems. Each role is linked to specific impacts, emphasizing the importance of well-functioning laboratory services.

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 and theoretical percentages as estimated by [6,7,8,10,16,22,24,25].

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

• 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. 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 understand the patient’s condition clearly. 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.
• 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.

Figure 3 is a flow chart illustrating the laboratory testing process leading to clinical decision-making. A significant proportion of clinical decisions are shaped by the results of laboratory medicine [6,8]. Consequently, professionals in this field are responsible for ensuring the laboratory is utilized effectively.

The axes represent the following:

Horizontal axis (X-axis): This axis typically represents the sequential steps or stages in the laboratory testing process. It shows 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 each stage’s relative importance or impact 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.

Collaborative healthcare emerges when healthcare professionals from diverse backgrounds unite to give services, working alongside patients and caregivers to deliver quality care across conventional settings [7,8,12]. This collaborative approach introduces an additional layer of responsibility for specialists in laboratory medicine.

3.3. Accessing Healthcare Through Laboratory Services

Access to diagnostic services in LMICs like Africa is severely limited due to a lack of human, financial, and technical resources, negatively impacting various aspects of the healthcare system [7,8,26]. The absence of standardized protocols for evaluating and registering diagnostic tools further complicates the integration of advanced technologies, resulting in missed opportunities to address critical healthcare challenges.

This systematic review indicates the consequences of restricted diagnostic access in LMICs. For instance, in Africa, despite robust vertical control programs, 40% of HIV-positive individuals receiving antiretroviral therapy do not participate in the recommended annual monitoring of their viral load [27]. Additionally, in 2016, 21% of infants born to HIV-positive mothers in West and Central Africa did not receive early diagnosis testing before eight weeks of age [7,8,26].

Moreover, 70% of patients are unaware of their medical conditions and access to tuberculosis laboratory testing was limited to less than 10% of patients in 23 out of 47 countries [26]. Second-line tuberculosis testing was available in only 60% of African nations [21]. The situation is similarly dire for diseases lacking specific support programs; for example, 90% of individuals infected with hepatitis B or C have never been tested, despite these infections accounting for 60% of liver cancer cases [7,8,26].

A similar study conducted in a West African country from 2015 to 2016 found that fewer than 30% of pregnant women receiving antenatal care at primary healthcare facilities had access to essential screening tests for common conditions threatening maternal and child health [21]. The findings indicate that nearly half of cervical cancer-related deaths were due to late detection of the disease. Ondoa (2020) [26] observed that, apart from focused investments in specific diseases like HIV and tuberculosis, national laboratory networks are predominantly underfunded and demonstrate varying levels of dysfunction within their foundational structures. The insufficient capacity of these laboratory networks not only restricts access to vital clinical diagnostics but also compromises the health security framework’s detection component, including prevention, detection, and response [26]. As a result of this restricted access to medical diagnosis, the World Health Organization estimates that approximately 630 million years of healthy life are lost annually in Africa due to illness, resulting in an economic impact exceeding 2.4 trillion international dollars, equivalent in purchasing power to the U.S. dollar [21].

Consequently, limited access to in vitro diagnostics (IVDs) in LMICs significantly hinders the provision of life-saving treatments, diminishes the quality of healthcare services, and obstructs progress toward Universal Health Coverage in LMICs like Africa. Adopting simplified, robust, cost-effective diagnostic solutions enhances access in resource-limited settings [7,8,26]. Rapid diagnostic tests, which can be conducted at the community level or through self-testing, have revolutionized the management and prevention of various diseases, including HIV, malaria, and diabetes. However, conventional laboratory screening by point-of-care molecular technologies, such as molecular diagnostic devices, has shown improvements in testing specificity and convenience while reducing turnaround times for results, thereby enhancing patient retention and management [7,26].

The limited reach of diagnostics to many individuals in LMICs could be attributed to implementation strategies that focus on site-level operations, neglecting the requirements of a tiered laboratory network and insufficient attention to foundational systems, including supply chain, workforce, and resource limitation [7,8,26]. The Maputo Declaration of 2008 and the Freetown Declaration of 2015 emphasize that providing diagnostic services within functional, integrated national laboratory networks is the most effective approach to achieving broad population coverage and cost-efficient delivery of diagnostic services in resource-constrained environments.

The African Society for Laboratory Medicine (ASLM) and the Africa Centres for Disease Control and Prevention (Africa CDC) identify key areas for improving laboratory network capacities in line with diagnostic technologies. Despite the urgent need for enhanced public health laboratory systems following the 2015 Ebola outbreak, there is a significant lack of data to assess the overall performance of national laboratory networks across various diseases. This information gap obstructs the development of effective interventions and limits the implementation of a wide range of relevant diagnostic technologies [7,8,26].

This gap underscores the urgent need for a comprehensive evaluation of laboratory systems to ensure accurate diagnoses and effective prevention strategies. Obeagu and Obeagu (2024) [11] identify barriers that hinder accurate diagnoses and propose strategies to enhance these systems, thereby improving prevention efforts. The integration of molecular testing, the importance of point-of-care diagnostics, the establishment of quality assurance programs, initiatives to build the capacity of laboratory personnel, funding strategies, and infrastructure enhancements were key recommendations for healthcare service improvement [7,8,18,26].

3.4. Quality Management System in Medical Laboratories—A Cost-Effective Model

This article offers a cost-effective model for QMS in MLS and LMICs that contribute to sustainable healthcare systems. Quality Management Systems (QMSs) 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. A 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. The following are some key elements of the ISO 15189:2022 QMS [28], detailing practices and strategies that laboratories use to maintain high QC standards. The key elements listed below constitute a cost-effective model for QMSs in MLS and LMICs that contribute to sustainable healthcare systems.

• Standard operating procedures (SOPs): A set of step-by-step instructions compiled by an organization to help workers to carry out routine operations. SOPs aim to achieve efficiency, quality output, and uniformity of performance while reducing miscommunication and failure to comply with industry regulations. Laboratories develop and adhere to detailed SOPs for all testing processes to ensure consistency and accuracy in test procedures.
• Calibration and maintenance of equipment: Process of determining the relationship between the output or response of a measuring instrument and the value of the input. Calibration typically involves the use of a measuring standard. Maintenance refers to functions or actions required to ensure the proper working order of a piece of equipment. Laboratory equipment is regularly calibrated and maintained to ensure correct function and accurate results.
• Internal quality control (IQC): Process of determining the relationship 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 to detect trends, shifts, or unusual variations in test results.
• External quality assessment (EQA): Used to periodically assess the quality of a lab’s performance and achieve added confidence in patient test results. Results are objectively compared to other laboratories using the same methodologies, instruments, and reagents. Participation in external proficiency testing programs where laboratories analyze unknown samples from an external agency to provide an independent assessment of laboratory performance and identify areas for improvement.
• Staff training and competency assessment: laboratory personnel undergo continuous training and competency assessments, ensuring that they are skilled and knowledgeable about the latest techniques and standards.
• Documentation and record keeping: having detailed documentation of all procedures, test results, and quality control measures facilitates traceability and accountability and helps to identify and correct errors.
• Regular audits and reviews: conduct regular internal and external audits that review compliance with quality standards to identify gaps and areas for improvement in the laboratory’s quality control processes.
• Corrective and preventive actions (CAPAs): implementing CAPA processes that address any identified issues and prevent recurrence enhances laboratory services’ overall quality and reliability.
• Risk management: involves identifying and managing potential risks that could impact the quality of laboratory results, as well as proactively addressing issues before they affect test outcomes.

Quality assurance proactively ensures the reliability and accuracy of laboratory tests, maintaining high standards of care and patient safety. Education and training build a knowledgeable workforce that improves healthcare delivery [14,17,18].

By implementing these quality management measures, laboratories can ensure that they provide accurate, reliable, and timely test results, crucial for effective patient care and clinical decision-making. Implementing a QMS, particularly ISO 15189: 2022 requirements for public health laboratories [28], is the panacea for effective and efficient service delivery in healthcare [10,14,17,18].

3.5. The Roles of Artificial Intelligence Technologies in Modern Laboratory Medicine

The adoption of artificial intelligence (AI) addresses research questions 3, 4, and 5. AI exhibits exceptional accuracy in analyzing medical images and predicting patient outcomes using extensive datasets [7,14]. 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, inadequate digital infrastructure, and ethical concerns [7,14,29].

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 [7,8,14].

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 [7,14]. 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 research institutions is essential for knowledge exchange and capacity development [7,8,14].

Transformative Capabilities of Artificial Intelligence in Medical Laboratories

This systematic 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 analytic 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].
• 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 to address the 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.

3.6. Collaboration Among Hospital Clinicians and Medical Laboratory Scientists

Hospital clinicians play a crucial role in shaping the range of tests and investigations offered by laboratory medicine services, making them key stakeholders in collaborative healthcare [30]. The selection of tests is influenced by various factors, including the specific clinical specialties involved and the balance between acute and non-acute services. It has been suggested that laboratory medicine specialists should build strong relationships with clinical leads across specialties. This collaboration modernizes the test repertoire and fosters joint initiatives that enhance the value of laboratory medicine for patients [7,8].

Hospital clinicians expect laboratory results to meet high analytical standards; however, they may not fully appreciate the importance of the preanalytical phase, which they can influence as a critical quality component [2]. Additionally, they might be unaware that variability in testing methods can impact result transferability and the local application of national clinical guidelines. Therefore, laboratory specialists must communicate the significance of method harmonization and traceability to clinicians as part of their collaborative efforts [7,8].

While senior clinicians generally understand the clinical implications of laboratory results, junior doctors and other healthcare professionals may need assistance interpreting these findings. Support can take various forms, such as providing interpretive comments on reports, issuing laboratory alerts, and ensuring that laboratory specialists are available for on-demand discussions about results. Research indicates that such interpretive support improves the timeliness and quality of diagnoses [7,8]. Effective communication is vital for optimizing the collaborative relationship between clinical laboratories and their hospital users [3]. Laboratory professionals can offer clinical colleagues advice on test selection and interpretation of laboratory results by combining their unique ability to perform quality laboratory assays with knowledge of the interpretation of tests [7,8,12].

4. Discussion

This systematic review titled “Medical Laboratories in Healthcare Delivery: A Systematic 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. Below 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, as illustrated in Table 1. 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, and molecular biology. These tests help in diagnosing infections, metabolic disorders, malignancies, 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 to control the virus’s spread and manage the pandemic more effectively.

4.1.3. Clinical Decision Support

As illustrated in Figure 3, 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 in minimizing 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 accurately analyze medical images, 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 molecular diagnostic devices 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 (QMSs)

Implementing a QMS, particularly ISO 15189:2022 [28], 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 multifaceted approach. The following are some actionable policy recommendations:

4.4.1. Quality Management Systems (QMSs)

• Adopt scalable frameworks: Implement scalable QMS frameworks like ISO 9001, which can be tailored to the specific needs and capacities of LMICs [31]. 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 implementation [32].
• Public–private partnerships: Encourage partnerships between governments and private sector entities to share the costs and benefits of implementing a 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. Strengthening the Positioning and Rationale for the Cost-Effective QMS Model in MLS

This discussion emphasizes the importance of a cost-effective Quality Management System (QMS) in medical laboratory science (MLS), particularly in LMICs. A robust QMS ensures laboratory test results’ accuracy, reliability, and timeliness, 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 [33,34].

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 [8,14].

Practical recommendations include adopting the ISO 15189:2022 standard [28], which outlines 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 QMSs in resource-limited settings.

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. 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 healthcare 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 to 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.

4.7. Future Directions

4.7.1. 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 support healthcare delivery better and respond to emerging health threats.

4.7.2. 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.

4.7.3. 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.

4.7.4. Ongoing Research and Development

Ongoing research and development are crucial for the effective functioning of medical laboratories (MLs) in healthcare delivery. Researchers consistently highlight the critical role of well-equipped public health laboratories in improving healthcare outcomes [26,35]. With a well-trained workforce, set standards, and Quality Management System (QMS) implementation, MLS is integral to health system strengthening. Clinical diagnostic insights are essential for population health management, as they help to identify surveillance, prevention, and treatment needs [3,36]. Various stakeholders, including government agencies, healthcare organizations, professional associations, and diagnostic firms, play significant roles in this process [14,37,38]. MLs were pivotal in the early detection of COVID-19 [39,40].

This systematic review identifies several strategies to address healthcare challenges related to MLS roles, such as acquiring conventional laboratory resources, strengthening regional clinical laboratory networks, installing mobile labs, and developing laboratory emergency plans [27,41,42]. The COVID-19 pandemic highlighted the importance of laboratory medicine during infectious disease outbreaks [7,14,43]. In LMICs, the absence of timely diagnostic and response capabilities in primary health centers is exacerbated by the incomplete integration of national laboratories with district-level facilities, leading to suboptimal operations [44].

Recent innovations suggest that an integrated network of laboratories can conduct essential diagnostic procedures, eliminating the need for multiple laboratory visits [20,29]. Recommendations for establishing a fully integrated laboratory network include providing fundamental laboratory testing with quality assurance, implementing unified specimen collection platforms, and enhancing the ability to adopt new technologies [21,45,46]. Challenges facing public health laboratories include insufficient infrastructure, weak connections with clinical services, inadequate quality control systems, and a lack of effective leadership [21,47].

Strategic optimization of laboratory capacity expansion and investment is necessary to address public health concerns, enhance healthcare, and support other priorities, including the resurgence of infectious and chronic diseases [4,21,47]. The technical challenges of developing diagnostic tests are compounded by complex and time-consuming processes. The absence of local diagnostic capabilities has led to significant delays in recognizing outbreaks, resulting in substantial loss of life and financial costs [4,7,8,48].

5. Future Directions in Laboratory Medicine

Integrating artificial intelligence (AI) in laboratory medicine transforms healthcare by enhancing diagnostic accuracy, efficiency, and patient outcomes. Ongoing research is crucial for developing AI technologies that are contextually relevant and can be effectively integrated into existing laboratory systems. Partnerships between healthcare providers, technology developers, and regulatory bodies are vital for overcoming barriers to AI implementation and ensuring its benefits are widely accessible. Here are a few future directions for MLS:

5.1. Enhanced Diagnostic Accuracy

Enhanced diagnostic accuracy for image analysis and predictive analytics.

• Image analysis: AI excels in analyzing medical images, such as radiographs and MRIs, achieving higher accuracy than traditional methods. This allows for earlier detection of conditions like cancer.
• Predictive analytics: AI analyzes vast datasets, including patient histories and laboratory results, to predict outcomes and suggest tailored treatment plans, which is particularly beneficial for managing chronic diseases [24,49].

5.2. Operational Efficiency

Operational efficiency in the automation of routine tasks and streamlined workflow.

• Automation of routine tasks: AI automates repetitive tasks in laboratories, such as sample sorting and data entry, freeing up professionals for more complex analyses.
• Streamlined workflow: AI optimizes laboratory workflows, reducing turnaround times for test results, which is crucial in emergencies where timely decisions can save [14,50].

5.3. Improved Decision Support

Improved decision support for Clinical Decision Support Systems (CDSS) and Integration with Electronic Health Records (EHRs).

• Clinical decision support systems (CDSSs): AI-driven CDSSs assist healthcare providers in selecting appropriate tests and interpreting results, ensuring clinical decisions are based on accurate data.
• Integration with electronic health records (EHRs): AI enhances EHR systems by providing insights and recommendations based on real-time data analysis, improving overall patient care [8,51].

5.4. Challenges and Considerations

The challenges and considerations are related to data quality and availability as well as ethical and regulatory issues.

• Data quality and availability: The effectiveness of AI relies on the quality and quantity of data. In many LMICs, data scarcity and poor digital infrastructure pose significant challenges.
• Ethical and regulatory issues: AI raises ethical questions regarding data privacy, security, and potential bias in algorithms. Establishing robust regulatory frameworks is essential to address these concerns [14,52].

5.5. Latest Advancements in Laboratory Technology

The latest advancements in laboratory technology include Automation and Robotics, artificial intelligence (AI) and Data Analytics, Digital Pathology, High-Throughput Screening, Green Laboratory Practices, Telemedicine and Remote Collaboration Tools, CRISPR and Gene Editing Technologies, and Biomaterials and Regenerative Medicine.

• Automation and robotics: Streamlining processes, enhancing efficiency, and reducing human error. Automated systems handle repetitive tasks, allowing scientists to focus on complex analyses.
• Artificial intelligence (AI) and data analytics: AI is used for data analysis, helping to interpret complex datasets and improve diagnostic accuracy. Machine learning algorithms identify patterns in lab results that are not immediately apparent to human analysts.
• Digital pathology: allows for digitizing pathology slides, enabling remote access and collaboration among pathologists, enhancing diagnostic capabilities, and facilitating quicker decision-making.
• Green laboratory practices: emphasizing sustainability in lab operations, including using eco-friendly materials and waste reduction strategies to minimize environmental impact.
• Telemedicine and remote collaboration tools: enabling remote consultations and collaboration among healthcare professionals, improving access to laboratory services.
• CRISPR and gene editing technologies: advancements in CRISPR technology enable precise genetic modifications, which have significant implications for research and therapeutic applications.

5.6. Summary of Review Findings

The article “Medical Laboratories in Healthcare Delivery: A Systematic Review of Their Roles and Impact” highlights the essential functions of MLs in enhancing global healthcare systems. It emphasizes their role in providing diagnostic services that facilitate early disease detection, inform clinical decision-making, and support public health initiatives, especially during pandemics. The article points out the following key points:

• Role in healthcare: MLs are crucial for accurate diagnostics, disease surveillance, and emergency response. They significantly improve patient outcomes by guiding treatment decisions through precise laboratory results.
• 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.
• Collaborative efforts: The article advocates collaboration among governments, healthcare organizations, and laboratory professionals to strengthen laboratory systems and develop robust infrastructures to meet modern healthcare demands.
• Future directions: This paper calls for investments in laboratory infrastructure, the adoption of innovative technologies, and the establishment of regulatory frameworks to enhance laboratory services. It emphasizes ongoing research and development to address evolving healthcare challenges.

MLs are indispensable in healthcare delivery, particularly in LMICs, where they play a vital role in minimizing disease impact, ensuring accurate diagnoses, and enhancing patient outcomes. Strategic improvements, including simplified diagnostic solutions and collaborative initiatives, are necessary to maximize their impact on public health.

6. Conclusions and Recommendations

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

• Knowledge gap in laboratory contributions: This 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, this 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: This 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: This 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, this 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, this 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, this systematic 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.

This systematic review underscores the indispensable role of MLs in healthcare delivery. MLs 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 (Figure 2 and Figure 3). Integrating innovative technologies, especially artificial intelligence, offers significant opportunities to enhance diagnostic accuracy and operational efficiency.

However, challenges such as inadequate infrastructure, insufficient data availability, and ethical concerns must be addressed to fully realize MLs’ potential. Collaborative efforts among healthcare stakeholders, including governments, organizations, and laboratory professionals, are crucial for developing robust laboratory systems that can meet healthcare’s evolving demands.

Investing in laboratory infrastructure, embracing technological advancements, and fostering a culture of collaboration will be vital for improving health outcomes globally. By ensuring that MLs are well-equipped and effectively integrated into healthcare systems, we can enhance the quality of care provided to patients and strengthen public health responses to emerging health threats.

To effectively harness the potential of AI, the below 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 tasks [8,53]. 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 productivity [3,54].

2.

Workforce training:

• AI skill 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 them [55].
• AI maturity levels: progress through AI maturity levels, from awareness to transformational stages, to fully integrate AI into business processes.

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 output [9].
• 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 [56].

The below 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 skill 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 to drive effective AI adoption and maximize its benefits.

Laboratory medicine is essential for effective healthcare delivery; it enhances healthcare systems in various ways, primarily by providing vital diagnostic services that facilitate the early detection, diagnosis, and monitoring of diseases. This, in turn, informs clinical decision-making, treatment choices, patient management, and the provision of precise and timely laboratory test results that inform clinical treatment and desired health outcomes for patients. It is possible to develop long-term disease surveillance programs in LMICs by developing new diagnostic technologies that are accessible and affordable through well-equipped MLs to improve diagnostic readiness. A thorough and coordinated strategy is necessary to unlock AI’s transformative capabilities and enhance healthcare in LMICs [57].