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Diagnostics
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AI-Driven Diagnostics: Transforming Healthcare from Data to Clinical Decisions
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AI-Driven Diagnostics: Transforming Healthcare from Data to Clinical Decisions

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 3516

Special Issue Editors

Dr. Mobeen Ur Rehman

E-Mail Website
Guest Editor
Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
Interests: artificial intelligence; bioinformatics; computational biology; medical imaging; pattern recognition
Prof. Dr. Kil-To Chong

E-Mail Website
Guest Editor
Advances Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Republic of Korea
Interests: computational biology; artificial intelligence; bioinformatics; drug discovery; deep learning

Special Issue Information

Dear Colleagues,

This special issue of MDPI Diagnostics focuses on the transformational impact of artificial intelligence (AI) in healthcare diagnostics. The use of AI into diagnostic tools has the potential to change healthcare by improving diagnostic accuracy, efficiency, and accessibility, thus improving patient outcomes.

The articles in this special issue cover a wide range of AI-driven diagnostics-related topics, such as the development and validation of novel AI-based diagnostic tools, the integration of AI into medical imaging and pathology, personalized medicine and precision diagnostics, ethical considerations, comparative studies, case studies, challenges and limitations, and the potential impact of AI-driven diagnostics on healthcare systems.

The goal of this special issue is to encourage academics, doctors, and policymakers to investigate the possibilities of artificial intelligence in increasing diagnostic accuracy, efficiency, and patient outcomes, while also contemplating the ethical implications of this technology. We accept manuscripts of all forms that investigate the most recent breakthroughs in AI-driven diagnostics and their potential to improve healthcare.

We believe that this special issue will help advance the area of AI-driven diagnostics and pave the way for more creative solutions in the future, resulting in improved patient care and results.

Dr. Mobeen Ur Rehman
Prof. Dr. Kil-To Chong
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • healthcare systems
  • deep learning
  • big data
  • medical imaging
  • personalized medicine
  • precision diagnostics
  • clinical decision support
  • comparative studies
  • healthcare systems
  • genomics
  • digital pathology
  • diagnostics
  • computational biology
  • bioinformatics

Published Papers (5 papers)

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Research

11 pages, 849 KiB  
Article
The Role of Large Language Models (LLMs) in Providing Triage for Maxillofacial Trauma Cases: A Preliminary Study
by Andrea Frosolini, Lisa Catarzi, Simone Benedetti, Linda Latini, Glauco Chisci, Leonardo Franz, Paolo Gennaro and Guido Gabriele
Diagnostics 2024, 14(8), 839; https://doi.org/10.3390/diagnostics14080839 - 18 Apr 2024
Viewed by 406
Abstract
Background: In the evolving field of maxillofacial surgery, integrating advanced technologies like Large Language Models (LLMs) into medical practices, especially for trauma triage, presents a promising yet largely unexplored potential. This study aimed to evaluate the feasibility of using LLMs for triaging complex [...] Read more.
Background: In the evolving field of maxillofacial surgery, integrating advanced technologies like Large Language Models (LLMs) into medical practices, especially for trauma triage, presents a promising yet largely unexplored potential. This study aimed to evaluate the feasibility of using LLMs for triaging complex maxillofacial trauma cases by comparing their performance against the expertise of a tertiary referral center. Methods: Utilizing a comprehensive review of patient records in a tertiary referral center over a year-long period, standardized prompts detailing patient demographics, injury characteristics, and medical histories were created. These prompts were used to assess the triage suggestions of ChatGPT 4.0 and Google GEMINI against the center’s recommendations, supplemented by evaluating the AI’s performance using the QAMAI and AIPI questionnaires. Results: The results in 10 cases of major maxillofacial trauma indicated moderate agreement rates between LLM recommendations and the referral center, with some variances in the suggestion of appropriate examinations (70% ChatGPT and 50% GEMINI) and treatment plans (60% ChatGPT and 45% GEMINI). Notably, the study found no statistically significant differences in several areas of the questionnaires, except in the diagnosis accuracy (GEMINI: 3.30, ChatGPT: 2.30; p = 0.032) and relevance of the recommendations (GEMINI: 2.90, ChatGPT: 3.50; p = 0.021). A Spearman correlation analysis highlighted significant correlations within the two questionnaires, specifically between the QAMAI total score and AIPI treatment scores (rho = 0.767, p = 0.010). Conclusions: This exploratory investigation underscores the potential of LLMs in enhancing clinical decision making for maxillofacial trauma cases, indicating a need for further research to refine their application in healthcare settings. Full article
(This article belongs to the Special Issue AI-Driven Diagnostics: Transforming Healthcare from Data to Clinical Decisions)
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18 pages, 1906 KiB  
Article
Analyzing Longitudinal Health Screening Data with Feature Ensemble and Machine Learning Techniques: Investigating Diagnostic Risk Factors of Metabolic Syndrome for Chronic Kidney Disease Stages 3a to 3b
by Ming-Shu Chen, Tzu-Chi Liu, Mao-Jhen Jhou, Chih-Te Yang and Chi-Jie Lu
Diagnostics 2024, 14(8), 825; https://doi.org/10.3390/diagnostics14080825 - 17 Apr 2024
Viewed by 386
Abstract
Longitudinal data, while often limited, contain valuable insights into features impacting clinical outcomes. To predict the progression of chronic kidney disease (CKD) in patients with metabolic syndrome, particularly those transitioning from stage 3a to 3b, where data are scarce, utilizing feature ensemble techniques [...] Read more.
Longitudinal data, while often limited, contain valuable insights into features impacting clinical outcomes. To predict the progression of chronic kidney disease (CKD) in patients with metabolic syndrome, particularly those transitioning from stage 3a to 3b, where data are scarce, utilizing feature ensemble techniques can be advantageous. It can effectively identify crucial risk factors, influencing CKD progression, thereby enhancing model performance. Machine learning (ML) methods have gained popularity due to their ability to perform feature selection and handle complex feature interactions more effectively than traditional approaches. However, different ML methods yield varying feature importance information. This study proposes a multiphase hybrid risk factor evaluation scheme to consider the diverse feature information generated by ML methods. The scheme incorporates variable ensemble rules (VERs) to combine feature importance information, thereby aiding in the identification of important features influencing CKD progression and supporting clinical decision making. In the proposed scheme, we employ six ML models—Lasso, RF, MARS, LightGBM, XGBoost, and CatBoost—each renowned for its distinct feature selection mechanisms and widespread usage in clinical studies. By implementing our proposed scheme, thirteen features affecting CKD progression are identified, and a promising AUC score of 0.883 can be achieved when constructing a model with them. Full article
(This article belongs to the Special Issue AI-Driven Diagnostics: Transforming Healthcare from Data to Clinical Decisions)
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13 pages, 4675 KiB  
Article
Performance Assessment of ChatGPT versus Bard in Detecting Alzheimer’s Dementia
by Balamurali B.T and Jer-Ming Chen
Diagnostics 2024, 14(8), 817; https://doi.org/10.3390/diagnostics14080817 - 15 Apr 2024
Viewed by 472
Abstract
Large language models (LLMs) find increasing applications in many fields. Here, three LLM chatbots (ChatGPT-3.5, ChatGPT-4, and Bard) are assessed in their current form, as publicly available, for their ability to recognize Alzheimer’s dementia (AD) and Cognitively Normal (CN) individuals using textual input [...] Read more.
Large language models (LLMs) find increasing applications in many fields. Here, three LLM chatbots (ChatGPT-3.5, ChatGPT-4, and Bard) are assessed in their current form, as publicly available, for their ability to recognize Alzheimer’s dementia (AD) and Cognitively Normal (CN) individuals using textual input derived from spontaneous speech recordings. A zero-shot learning approach is used at two levels of independent queries, with the second query (chain-of-thought prompting) eliciting more detailed information than the first. Each LLM chatbot’s performance is evaluated on the prediction generated in terms of accuracy, sensitivity, specificity, precision, and F1 score. LLM chatbots generated a three-class outcome (“AD”, “CN”, or “Unsure”). When positively identifying AD, Bard produced the highest true-positives (89% recall) and highest F1 score (71%), but tended to misidentify CN as AD, with high confidence (low “Unsure” rates); for positively identifying CN, GPT-4 resulted in the highest true-negatives at 56% and highest F1 score (62%), adopting a diplomatic stance (moderate “Unsure” rates). Overall, the three LLM chatbots can identify AD vs. CN, surpassing chance-levels, but do not currently satisfy the requirements for clinical application. Full article
(This article belongs to the Special Issue AI-Driven Diagnostics: Transforming Healthcare from Data to Clinical Decisions)
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19 pages, 6984 KiB  
Article
Improving Respiratory Infection Diagnosis with Deep Learning and Combinatorial Fusion: A Two-Stage Approach Using Chest X-ray Imaging
by Cheng-Tang Pan, Rahul Kumar, Zhi-Hong Wen, Chih-Hsuan Wang, Chun-Yung Chang and Yow-Ling Shiue
Diagnostics 2024, 14(5), 500; https://doi.org/10.3390/diagnostics14050500 - 26 Feb 2024
Viewed by 727
Abstract
The challenges of respiratory infections persist as a global health crisis, placing substantial stress on healthcare infrastructures and necessitating ongoing investigation into efficacious treatment modalities. The persistent challenge of respiratory infections, including COVID-19, underscores the critical need for enhanced diagnostic methodologies to support [...] Read more.
The challenges of respiratory infections persist as a global health crisis, placing substantial stress on healthcare infrastructures and necessitating ongoing investigation into efficacious treatment modalities. The persistent challenge of respiratory infections, including COVID-19, underscores the critical need for enhanced diagnostic methodologies to support early treatment interventions. This study introduces an innovative two-stage data analytics framework that leverages deep learning algorithms through a strategic combinatorial fusion technique, aimed at refining the accuracy of early-stage diagnosis of such infections. Utilizing a comprehensive dataset compiled from publicly available lung X-ray images, the research employs advanced pre-trained deep learning models to navigate the complexities of disease classification, addressing inherent data imbalances through methodical validation processes. The core contribution of this work lies in its novel application of combinatorial fusion, integrating select models to significantly elevate diagnostic precision. This approach not only showcases the adaptability and strength of deep learning in navigating the intricacies of medical imaging but also marks a significant step forward in the utilization of artificial intelligence to improve outcomes in healthcare diagnostics. The study’s findings illuminate the path toward leveraging technological advancements in enhancing diagnostic accuracies, ultimately contributing to the timely and effective treatment of respiratory diseases. Full article
(This article belongs to the Special Issue AI-Driven Diagnostics: Transforming Healthcare from Data to Clinical Decisions)
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10 pages, 2221 KiB  
Article
Deep-Learning-Based Automated Rotator Cuff Tear Screening in Three Planes of Shoulder MRI
by Kyu-Chong Lee, Yongwon Cho, Kyung-Sik Ahn, Hyun-Joon Park, Young-Shin Kang, Sungshin Lee, Dongmin Kim and Chang Ho Kang
Diagnostics 2023, 13(20), 3254; https://doi.org/10.3390/diagnostics13203254 - 19 Oct 2023
Cited by 2 | Viewed by 1018
Abstract
This study aimed to develop a screening model for rotator cuff tear detection in all three planes of routine shoulder MRI using a deep neural network. A total of 794 shoulder MRI scans (374 men and 420 women; aged 59 ± 11 years) [...] Read more.
This study aimed to develop a screening model for rotator cuff tear detection in all three planes of routine shoulder MRI using a deep neural network. A total of 794 shoulder MRI scans (374 men and 420 women; aged 59 ± 11 years) were utilized. Three musculoskeletal radiologists labeled the rotator cuff tear. The YOLO v8 rotator cuff tear detection model was then trained; training was performed with all imaging planes simultaneously and with axial, coronal, and sagittal images separately. The performances of the models were evaluated and compared using receiver operating curves and the area under the curve (AUC). The AUC was the highest when using all imaging planes (0.94; p < 0.05). Among a single imaging plane, the axial plane showed the best performance (AUC: 0.71), followed by the sagittal (AUC: 0.70) and coronal (AUC: 0.68) imaging planes. The sensitivity and accuracy were also the highest in the model with all-plane training (0.98 and 0.96, respectively). Thus, deep-learning-based automatic rotator cuff tear detection can be useful for detecting torn areas in various regions of the rotator cuff in all three imaging planes. Full article
(This article belongs to the Special Issue AI-Driven Diagnostics: Transforming Healthcare from Data to Clinical Decisions)
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