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Diagnostics
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The Application of Large Language Models (LLMs) and Vision-Language Models...
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The Application of Large Language Models (LLMs) and Vision-Language Models (VLMs) in Healthcare

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 May 2026 | Viewed by 1953

Special Issue Editors

Dr. Shuo-Chen Chien

E-Mail Website
Guest Editor
Department of Software Engineering and Management, National Kaohsiung Normal University, Kaohsiung, Taiwan
Interests: artificial intelligence; data visualization; natural language processing; CDSS alert system
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wen-Shan Jian

E-Mail Website
Guest Editor
School of Health Care Administration, Taipei Medical University, Taipei 11031, Taiwan
Interests: Internet of Things; healthcare management; artificial intelligence; data visualization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid development of Large Language Models (LLMs) and Vision-Language Models (VLMs) is reshaping the landscape of healthcare research and practice. These models enable new levels of performance in natural language understanding, multimodal reasoning, and clinical data interpretation. Their applications range from automated clinical documentation and medical imaging analysis to personalized treatment recommendations and healthcare management, offering both opportunities and challenges for integration into real-world clinical environments.

This Special Issue invites submissions that explore theoretical advances, practical implementations, and case studies on the application of LLMs and VLMs in healthcare. Topics of interest include, but are not limited to, natural language processing for electronic health records, multimodal analysis for medical imaging and diagnostics, clinical decision support, healthcare informatics, and AI-driven patient engagement. We also welcome contributions addressing issues of interpretability, fairness, ethics, and governance in the deployment of these technologies.

By contributing to this Special Issue, authors will help illuminate the current progress and future directions of AI in healthcare, fostering a multidisciplinary dialogue that connects researchers, clinicians, and policymakers. This collaborative effort aims to accelerate innovation while ensuring safe, equitable, and impactful adoption of LLMs and VLMs in healthcare systems worldwide.

Dr. Shuo-Chen Chien
Prof. Dr. Wen-Shan Jian
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. 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

  • large language models (LLMs)
  • vision-language models (VLMs)
  • artificial intelligence in healthcare
  • natural language processing (NLP)
  • clinical decision support systems
  • medical imaging analysis
  • multimodal learning
  • healthcare data analytics
  • patient-centered care
  • ethical AI in medicine

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Published Papers (1 paper)

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Research

16 pages, 650 KB  
Article
Evaluating Medical Text Summaries Using Automatic Evaluation Metrics and LLM-as-a-Judge Approach: A Pilot Study
by Yuriy Vasilev, Irina Raznitsyna, Anastasia Pamova, Tikhon Burtsev, Tatiana Bobrovskaya, Pavel Kosov, Anton Vladzymyrskyy, Olga Omelyanskaya and Kirill Arzamasov
Diagnostics 2026, 16(1), 3; https://doi.org/10.3390/diagnostics16010003 - 19 Dec 2025
Viewed by 1522
Abstract
Background: Electronic health records (EHRs) remain a vital source of clinical information, yet processing these heterogeneous data is extremely labor-intensive. Summarization of these data using Large Language Models (LLMs) is considered a promising tool to support practicing physicians. Unbiased, automated quality control is [...] Read more.
Background: Electronic health records (EHRs) remain a vital source of clinical information, yet processing these heterogeneous data is extremely labor-intensive. Summarization of these data using Large Language Models (LLMs) is considered a promising tool to support practicing physicians. Unbiased, automated quality control is crucial for integrating the tools into routine practice, saving time and labor. This pilot study aimed to assess the potential and constraints of self-contained evaluation of summarization quality (without expert involvement) based on automatic evaluation metrics and LLM-as-a-judge. Methods: The summaries of text data from 30 EHRs were generated by six open-source low-parameter LLMs. The medical summaries were evaluated using standard automatic metrics (BLEU, ROUGE, METEOR, BERTScore) as well as the LLM-as-a-judge approach using the following criteria: relevance, completeness, redundancy, coherence and structure, grammar and terminology, and hallucinations. Expert evaluation was conducted using the same criteria. Results: The results showed that LLMs hold great promise for summarizing medical data. Nevertheless, neither the evaluation metrics nor LLM judges are reliable in detecting factual errors and semantic distortions (hallucinations). In terms of relevance, the Pearson correlation between the summary quality score and the expert opinions was 0.688. Conclusions: Completely automating the evaluation of medical summaries remains challenging. Further research should focus on dedicated methods for detecting hallucinations, along with investigating larger or specialized models trained on medical texts. Additionally, the potential integration of retrieval-augmented generation (RAG) within the LLM-as-a-judge architecture deserves attention. Nevertheless, even now, the combination of LLMs and the automatic evaluation metrics can underpin medical decision support systems by performing initial evaluations and highlighting potential shortcomings for expert review. Full article
(This article belongs to the Special Issue The Application of Large Language Models (LLMs) and Vision-Language Models (VLMs) in Healthcare)
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