Artificial Intelligence in Medical Diagnosis and Prognosis

Editor

Topical Collection Information

Dear Colleagues,

This is a collection of open access high-quality papers published by Editorial Board Members, or those who were invited by the Editorial Office. This Topical Collection aims to publish high-quality articles within the field of artificial intelligence in medical diagnosis and prognosis. The papers should be long research papers (or review papers) with full and detailed summaries of the author's own work performed so far. Please note that the selected full papers will still be subjected to thorough and rigorous peer review. All papers will be published on an ongoing basis.

Prof. Dr. Tim Duong
Collection 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 collection 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
  • diagnosis
  • prognosis
  • biomedical imaging
  • radiology

Published Papers (1 paper)

2024

16 pages, 1344 KiB  
Article
Evaluating Large Language Model (LLM) Performance on Established Breast Classification Systems
by Syed Ali Haider, Sophia M. Pressman, Sahar Borna, Cesar A. Gomez-Cabello, Ajai Sehgal, Bradley C. Leibovich and Antonio Jorge Forte
Diagnostics 2024, 14(14), 1491; https://doi.org/10.3390/diagnostics14141491 - 11 Jul 2024
Viewed by 1464
Abstract
Medical researchers are increasingly utilizing advanced LLMs like ChatGPT-4 and Gemini to enhance diagnostic processes in the medical field. This research focuses on their ability to comprehend and apply complex medical classification systems for breast conditions, which can significantly aid plastic surgeons in [...] Read more.
Medical researchers are increasingly utilizing advanced LLMs like ChatGPT-4 and Gemini to enhance diagnostic processes in the medical field. This research focuses on their ability to comprehend and apply complex medical classification systems for breast conditions, which can significantly aid plastic surgeons in making informed decisions for diagnosis and treatment, ultimately leading to improved patient outcomes. Fifty clinical scenarios were created to evaluate the classification accuracy of each LLM across five established breast-related classification systems. Scores from 0 to 2 were assigned to LLM responses to denote incorrect, partially correct, or completely correct classifications. Descriptive statistics were employed to compare the performances of ChatGPT-4 and Gemini. Gemini exhibited superior overall performance, achieving 98% accuracy compared to ChatGPT-4’s 71%. While both models performed well in the Baker classification for capsular contracture and UTSW classification for gynecomastia, Gemini consistently outperformed ChatGPT-4 in other systems, such as the Fischer Grade Classification for gender-affirming mastectomy, Kajava Classification for ectopic breast tissue, and Regnault Classification for breast ptosis. With further development, integrating LLMs into plastic surgery practice will likely enhance diagnostic support and decision making. Full article
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