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Artificial Intelligence Revolution in Chronic Inflammatory Diseases: Diagnosis, Prognosis, and...
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Artificial Intelligence Revolution in Chronic Inflammatory Diseases: Diagnosis, Prognosis, and Future Perspectives

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: closed (30 April 2025) | Viewed by 776

Special Issue Editors

Dr. Francesco Borgia

E-Mail Website
Guest Editor
Department of Clinical and Experimental Medicine, Section of Dermatology, University of Messina, 98125 Messina, Italy
Interests: skin; basal cell carcinoma; squamous cell carcinoma; dermatology imaging; photodynamic therapy (PDT); phototherapy; skin cancer; biopsy
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sebastiano Gangemi

E-Mail Website
Guest Editor
Department of Clinical and Experimental Medicine, School and Operative Unit of Allergy and Clinical Immunology, University of Messina, 98125 Messina, Italy
Interests: mediators of inflammation; cytokines; biomarkers of oxidative stress; immunosenescence; immunogenetics; epigenetics; application of machine learning; deep learning in various fields of medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Chronic inflammatory diseases represent a significant global health challenge, requiring innovative approaches to improve diagnosis, prognosis and personalized therapeutic strategies for each patient. In this context, artificial intelligence (Al) represents an important recent resource in reshaping our understanding and management of chronic inflammatory diseases, both cutaneous and systemic. The proposed collection of articles will delve into the latest advances in the applications of Al for an early and accurate diagnosis of these diseases in order to guarantee timely therapeutic interventions. Al-based prognostic models, which integrate multiple data sources, will be studied for their potential to predict disease progression, and personalize treatment plans. Furthermore, the goal of this Special Issue is to address the evolving landscape of Al in chronic inflammatory diseases, offering insights into the ethical considerations, challenges, and regulatory aspects associated with the implementation of Al technologies in clinical practice. Authors will be encouraged to present new Al-based solutions, discuss real-world applications, and explore the integration of Al with other technologies, such as genomics and precision medicine. 

Dr. Francesco Borgia
Prof. Dr. Sebastiano Gangemi
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
  • chronic inflammatory diseases
  • inflammation

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Published Papers (1 paper)

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Research

14 pages, 245 KiB  
Article
Evaluation of the Performance of Large Language Models in the Management of Axial Spondyloarthropathy: Analysis of EULAR 2022 Recommendations
by Ahmet Usen and Ozlem Kuculmez
Diagnostics 2025, 15(12), 1455; https://doi.org/10.3390/diagnostics15121455 - 7 Jun 2025
Viewed by 155
Abstract
Introduction: Guidelines have great importance in revealing complex and chronic conditions such as axial spondyloarthropathy. The aim of this study is to compare the answers given by various large language models to open-ended questions created from ASAS–EULAR 2022 guidance. Materials and Methods [...] Read more.
Introduction: Guidelines have great importance in revealing complex and chronic conditions such as axial spondyloarthropathy. The aim of this study is to compare the answers given by various large language models to open-ended questions created from ASAS–EULAR 2022 guidance. Materials and Methods: This was a cross-sectional and comparative study. A total of 15 recommendations in the ASAS–EULAR 2022 guideline were derived directly from their content into open-ended questions in a clinical context. Each question was asked to the ChatGPT-3.5, GPT-4o, and Gemini 2.0 Flash models, and the answers were evaluated with a seven-point Likert system in terms of usability, reliability, Flesch–Kincaid Reading Ease (FKRE) and Flesch–Kincaid Grade Level (FKGL) metrics for readability, Universal Sentence Encoder (USE) and ROUGE-L for semantic and surface-level similarity. The results of different large language models were statistically compared, and p < 0.05 was revealed as statistically significant. Results: Better FKRE and FKGL scores were obtained in the Google Gemini 2.0 program (p < 0.05). Reliability and usefulness scores were significantly higher for ChatGPT-4o and Gemini 2.0 (p < 0.05). ChatGPT-4o yielded significantly higher semantic similarity scores compared to ChatGPT-3.5 (p < 0.05). There was a negative correlation between FKRE and FKGL scores and a positive correlation between reliability and usefulness scores (p < 0.05). Conclusions: It was determined that ChatGPT-4o and Gemini 2.0 programs gave more reliable, useful, and readable answers to open-ended questions derived from the ASAS–EULAR 2022 guidelines. These programs may potentially assist in supporting treatment decision-making in rheumatology in the future. Full article
(This article belongs to the Special Issue Artificial Intelligence Revolution in Chronic Inflammatory Diseases: Diagnosis, Prognosis, and Future Perspectives)
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