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Artificial Intelligence in Clinical Medicine—Transforming Patient Care Through Innovation
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Artificial Intelligence in Clinical Medicine—Transforming Patient Care Through Innovation

A special issue of Reports (ISSN 2571-841X).

Deadline for manuscript submissions: closed (31 January 2026) | Viewed by 1281

Special Issue Editors

Dr. Ryo Ozuru

E-Mail Website
Guest Editor
School of Medicine, Fukuoka University, Fukuoka, Japan
Interests: AI in medicine; medical image analysis; deep learning; diagnostic automation; computational biology
Dr. Yuji Oyamada

E-Mail Website
Guest Editor
Graduate School of Engineering, Tottori University, Tottori, Japan
Interests: submodular function maximization; camera calibration; greedy algorithm; gaussian process; image selection
Prof. Dr. Toshio Hattori

E-Mail Website
Guest Editor
Emeritus Professor, International Research Institute of Disaster Science, Tohoku University, Sendai 980-8577, Miyagi, Japan
Interests: dengue; malaria; leptospirosis; HIV; tuberculosis; inflammation; galectins; osteopontin
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue of Reports  highlights the growing impact of artificial intelligence (AI) across clinical medicine, with a focus on real-world case studies, translational research, and critical reviews. We welcome submissions that demonstrate AI’s role in improving diagnosis, treatment, and healthcare delivery, particularly in the following areas:

  1. AI in Diagnostics and Precision Medicine
  • Medical Imaging and Pathology: AI-assisted radiology (CT, MRI, X-ray), digital pathology (WSI analysis), dermatology, and ophthalmology.
  • Genomics and Multi-Omics AI:
    • Clinical variant interpretation, polygenic risk prediction, and pharmacogenomics.
    • Microbiome and Metabolomics AI: disease biomarker discovery, host–microbiome interactions.
  • Rare and Complex Diseases: AI for early detection, differential diagnosis, and patient stratification.
  1. AI in Therapeutics and Personalized Medicine
  • Precision Oncology: AI-driven tumor profiling (TMB, MSI), immunotherapy response prediction.
  • Drug Discovery and Repurposing: deep learning for novel drug targets and combination therapies.
  • Digital Therapeutics: AI-powered interventions in mental health, neurology, and chronic disease management.
  1. Clinical Implementation and Ethical Considerations
  • NLP and Clinical Workflow: automated documentation (EHRs), real-time decision support.
  • Bias, Fairness and Regulatory AI: mitigating disparities in AI models, FDA/CE compliance.
  • Explainability and Clinician–AI Collaboration: ensuring trust and usability in real-world practice.

We particularly encourage case reports demonstrating AI’s impact on individual patient care (e.g., "AI-guided diagnosis of a rare genetic disorder") alongside validation studies and critical appraisals of AI failures in clinical settings. Submissions from clinician–AI researcher collaborations are highly valued.

Dr. Ryo Ozuru
Dr. Yuji Oyamada
Prof. Dr. Toshio Hattori
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 250 words) can be sent to the Editorial Office for assessment.

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. Reports is an international peer-reviewed open access quarterly 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 1400 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

  • AI in diagnostics and precision medicine
  • AI in therapeutics and personalized medicine
  • clinical implementation and ethical considerations
  • clinician–AI researcher collaborations

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

Published Papers (1 paper)

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Research

13 pages, 1912 KB  
Article
Accelerating Evidence Synthesis: A BERT-Assisted Workflow for Meta-Analyses of Radiotherapy Complications in Nasopharyngeal Carcinoma
by Tsair-Fwu Lee, Wen-Ping Yun, Hung-Wei Hsu, Jyun-Jie Wu, Ya-Shin Kuan, Yi-Lun Liao, Cheng-Shie Wuu, Liyun Chang, Yang-Wei Hsieh and Pei-Ju Chao
Reports 2026, 9(1), 90; https://doi.org/10.3390/reports9010090 - 18 Mar 2026
Viewed by 465
Abstract
Background/Objectives: This study developed and evaluated a BERT-assisted literature screening workflow to support meta-analyses of postradiotherapy complications in nasopharyngeal carcinoma patients. The aim was to automate key screening steps to improve downstream screening efficiency and consistency, while minimizing time and bias during [...] Read more.
Background/Objectives: This study developed and evaluated a BERT-assisted literature screening workflow to support meta-analyses of postradiotherapy complications in nasopharyngeal carcinoma patients. The aim was to automate key screening steps to improve downstream screening efficiency and consistency, while minimizing time and bias during manual reviews. Materials and Methods: A bidirectional encoder representations from transformers (BERT) model was integrated into a standard systematic review pipeline for studies on postradiotherapy complications in nasopharyngeal carcinoma. The workflow combined automated BERT-based classification with manual verification and followed PRISMA and PICOS guidelines for literature identification, screening, and eligibility assessment. Model training involved hyperparameter tuning and comparison of different optimizers to maximize screening performance against a manually curated reference set, with particular attention to discrimination (AUC) and processing time. Results: From an initial corpus of 6496 records, the combined automated and manual workflow identified 23 eligible studies for meta-analysis. The included studies showed substantial heterogeneity (I2 = 86.85%), supporting the use of a random-effects model to pool outcomes. The BERT model optimized with an Adagrad optimizer achieved an AUC of 0.77 for relevant-study classification and reduced screening time to 1142 s. To demonstrate the workflow’s utility, a downstream meta-analysis was conducted using the identified studies. As a downstream application based on the identified studies, a quantitative synthesis was conducted, in which (meta-analysis of the 23 included studies), a random forest model—evaluated across those studies—achieved an AUC of 0.92 under a fixed-effect analysis for predicting postradiotherapy complications. Conclusions: Integrating BERT into the literature screening phase of meta-analysis for postradiotherapy nasopharyngeal carcinoma complications markedly improved screening efficiency while maintaining acceptable classification performance. This workflow demonstrates the feasibility of transformer-based assistance for systematic reviews and provides a foundation for developing disease-specific, AI-augmented evidence synthesis pipelines in oncology. Full article
(This article belongs to the Special Issue Artificial Intelligence in Clinical Medicine—Transforming Patient Care Through Innovation)
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