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Diagnostics
Special Issues
AI-Powered Clinical Diagnosis and Decision-Support Systems
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AI-Powered Clinical Diagnosis and Decision-Support Systems

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 October 2025 | Viewed by 1957

Special Issue Editor

Prof. Dr. Hung-Sheng Shang

E-Mail Website
Guest Editor
Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
Interests: clinical pathology; molecular diagnosis; genome; epidemiology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is transforming clinical diagnosis and decision-making across medical disciplines. This Special Issue aims to showcase groundbreaking research that harnesses AI technologies to enhance clinicians' diagnostic precision, predictive abilities, and overall patient care.

We invite original research, comprehensive reviews, and case reports that explore the following:

  • AI applications in diagnostics, predictive modeling, and risk assessment;
  • Innovative AI methodologies, including machine learning, deep learning, natural language processing, and computer vision;
  • The integration of AI tools in electronic health records and clinical workflows;
  • Collaborative approaches between healthcare professionals and data scientists.

The Special Issue will highlight studies demonstrating tangible clinical impact, focusing on how AI can carry out the following:

  • Improve diagnostic accuracy;
  • Optimize patient outcomes;
  • Support complex medical decision-making;
  • Enhance healthcare system efficiency.

We particularly welcome multidisciplinary contributions that provide insights into the transformative potential of AI in contemporary healthcare, bridging technological innovation with clinical practice.

Prof. Dr. Hung-Sheng Shang
Guest Editor

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
  • clinical decision support system
  • medical diagnostics

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

13 pages, 1375 KiB  
Article
Deep Learning-Based Diagnosis of Lumbar Spondylolisthesis Using X-Ray Imaging
by Chunyang Xu, Yukan Wu, Beixi Bao, Xingyu Liu, Yiling Zhang, Runchao Li, Tianci Yang and Jiaguang Tang
Diagnostics 2025, 15(16), 2015; https://doi.org/10.3390/diagnostics15162015 - 12 Aug 2025
Viewed by 290
Abstract
Background: Lumbar spondylolisthesis (LS) is a common spinal disorder characterized by the forward displacement of the vertebra. Early detection is challenging due to asymptomatic presentation in the early stages. This study develops and validates an AI-based deep learning model for the early, [...] Read more.
Background: Lumbar spondylolisthesis (LS) is a common spinal disorder characterized by the forward displacement of the vertebra. Early detection is challenging due to asymptomatic presentation in the early stages. This study develops and validates an AI-based deep learning model for the early, high-precision diagnosis of LS using lumbar X-ray images. Methods: A total of 3300 lateral lumbar X-ray images were collected from Beijing Tongren Hospital, and an external dataset of 1100 images was used for validation. The images were randomly divided into the training, validation, and test sets. The model uses semantic segmentation to precisely segment vertebral bodies and calculate distances between vertebrae to identify and grade LS using the Meyerding classification. Model performance was compared to other algorithms and clinical experts. Results: The model achieved F1 Scores of 0.92 and 0.91 on the hospital and external datasets, respectively, outperforming other methods. It showed diagnostic accuracies of 96.1% and 94.4%, exceeding the performance of physicians (90.6% and 89.3%). These results highlight the potential of AI in improving diagnostic accuracy and clinical decision-making. Conclusions: Our deep learning model demonstrates high accuracy and reliability in diagnosing LS, providing a valuable tool for early detection and better patient outcomes. Future work will involve expanding the dataset and validating the model in clinical settings. Full article
(This article belongs to the Special Issue AI-Powered Clinical Diagnosis and Decision-Support Systems)
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16 pages, 1432 KiB  
Article
Transparent and Robust Artificial Intelligence-Driven Electrocardiogram Model for Left Ventricular Systolic Dysfunction
by Min Sung Lee, Jong-Hwan Jang, Sora Kang, Ga In Han, Ah-Hyun Yoo, Yong-Yeon Jo, Jeong Min Son, Joon-myoung Kwon, Sooyeon Lee, Ji Sung Lee, Hak Seung Lee and Kyung-Hee Kim
Diagnostics 2025, 15(15), 1837; https://doi.org/10.3390/diagnostics15151837 - 22 Jul 2025
Viewed by 469
Abstract
Background/Objectives: Heart failure (HF) is a growing global health burden, yet early detection remains challenging due to the limitations of traditional diagnostic tools such as electrocardiograms (ECGs). Recent advances in deep learning offer new opportunities to identify left ventricular systolic dysfunction (LVSD), a [...] Read more.
Background/Objectives: Heart failure (HF) is a growing global health burden, yet early detection remains challenging due to the limitations of traditional diagnostic tools such as electrocardiograms (ECGs). Recent advances in deep learning offer new opportunities to identify left ventricular systolic dysfunction (LVSD), a key indicator of HF, from ECG data. This study validates AiTiALVSD, our previously developed artificial intelligence (AI)-enabled ECG Software as a Medical Device, for its accuracy, transparency, and robustness in detecting LVSD. Methods: This retrospective single-center cohort study involved patients suspected of LVSD. The AiTiALVSD model, based on a deep learning algorithm, was evaluated against echocardiographic ejection fraction values. To enhance model transparency, the study employed Testing with Concept Activation Vectors (TCAV), clustering analysis, and robustness testing against ECG noise and lead reversals. Results: The study involved 688 participants and found AiTiALVSD to have a high diagnostic performance, with an AUROC of 0.919. There was a significant correlation between AiTiALVSD scores and left ventricular ejection fraction values, confirming the model’s predictive accuracy. TCAV analysis showed the model’s alignment with medical knowledge, establishing its clinical plausibility. Despite its robustness to ECG artifacts, there was a noted decrease in specificity in the presence of ECG noise. Conclusions: AiTiALVSD’s high diagnostic accuracy, transparency, and resilience to common ECG discrepancies underscore its potential for early LVSD detection in clinical settings. This study highlights the importance of transparency and robustness in AI-ECG, setting a new benchmark in cardiac care. Full article
(This article belongs to the Special Issue AI-Powered Clinical Diagnosis and Decision-Support Systems)
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Graphical abstract

11 pages, 2345 KiB  
Article
BioInnovate AI: A Machine Learning Platform for Rapid PCR Assay Design in Emerging Infectious Disease Diagnostics
by Hung-Hsin Lin, Hsing-Yi Chung, Tai-Han Lin, Chih-Kai Chang, Cherng-Lih Perng, Kuo-Sheng Hung, Katsunori Yanagihara, Hung-Sheng Shang and Ming-Jr Jian
Diagnostics 2025, 15(12), 1445; https://doi.org/10.3390/diagnostics15121445 - 6 Jun 2025
Viewed by 930
Abstract
Background/Objectives: Emerging infectious diseases pose significant global threats due to their rapid transmission, limited therapeutic options, and profound socioeconomic impact. Conventional diagnostic techniques that rely on sequencing and polymerase chain reactions (PCR) frequently lack the speed necessary to efficiently respond to rapidly evolving [...] Read more.
Background/Objectives: Emerging infectious diseases pose significant global threats due to their rapid transmission, limited therapeutic options, and profound socioeconomic impact. Conventional diagnostic techniques that rely on sequencing and polymerase chain reactions (PCR) frequently lack the speed necessary to efficiently respond to rapidly evolving pathogens. This study describes the development of BioInnovate AI to overcome these limitations using machine learning to expedite PCR assay development. Methods: The ability of BioInnovate AI to predict optimal PCR reagents across multiple pathogens was assessed. Additionally, random forest classifier, light gradient boosting machine (LGBM), and gradient boosting classifier models were evaluated for their ability to predict effective PCR primer–probe combinations. Performance metrics, including the area under the curve (AUC), sensitivity, specificity, accuracy, and F1 score, were assessed to identify the optimal model for platform integration. Results: All machine learning models performed well, with the LGBM model achieving the highest metrics (AUC: 0.97, sensitivity: 0.93, specificity: 0.91). BioInnovate AI significantly reduced PCR assay development time by approximately 90%, enabling rapid design and reagent optimization for multiple pathogens. Conclusions: BioInnovate AI provides a rapid, accurate, and efficient method for PCR reagent design, significantly enhancing global diagnostic preparedness by optimizing primers and probes for the timely detection of infectious diseases. Full article
(This article belongs to the Special Issue AI-Powered Clinical Diagnosis and Decision-Support Systems)
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