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 523

Special Issue Editor


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Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
Interests: clinical pathology; molecular diagnosis; genome; epidemiology
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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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Published Papers (1 paper)

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Research

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 335
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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