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Artificial Intelligence, Computer Modeling and Technology: Clinical Changing Practice in...
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Artificial Intelligence, Computer Modeling and Technology: Clinical Changing Practice in Reconstructive Surgery

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Plastic, Reconstructive and Aesthetic Surgery/Aesthetic Medicine".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 308

Special Issue Editors

Prof. Dr. Warren M. Rozen

E-Mail Website
Guest Editor
Faculty of Medicine and Surgery, Monash University, Melbourne, VIC 3004, Australia
Interests: breast surgery; breast reconstruction; oncologic surgery; skin cancer management; reconstruction; hand surgery
Special Issues, Collections and Topics in MDPI journals
Dr. Ishith Seth

E-Mail Website
Guest Editor
Faculty of Medicine and Surgery, Monash University, Melbourne, VIC 3004, Australia
Interests: microsurgery; oculoplastic surgery; medical education; breast surgery; applied artificial intelligence; hand surgery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We would like to invite you to contribute to our new Special Issue for JCM entitled ‘Artificial Intelligence, Computer Modeling and Technology: Clinical Changing Practice in Reconstructive Surgery’.

The integration of AI and computer learning into clinical medicine is revolutionizing the way medical treatment is delivered, enhancing both diagnostic accuracy and patient outcomes.

By analyzing vast amounts of medical treatment data, including medical histories, imaging results, and genetic information, machine learning algorithms can identify patterns and predict disease outcomes with remarkable precision. This technology enables personalized treatment plans tailored to individual patients, improving the efficacy of interventions while minimizing adverse effects. Additionally, computer learning aids in early disease detection and streamlining workflows. As these tools become increasingly sophisticated, they promise to augment the capabilities of clinical doctors, fostering a more proactive and data-driven approach to patient care.

We envisage that this Special Issue will highlight ongoing changes in current medical and reconstructive surgical management and will offer insights into the rapidly changing environment in this field.

Prof. Dr. Warren M. Rozen
Dr. Ishith Seth
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. Journal of Clinical Medicine 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

  • reconstructive surgery
  • plastic surgery
  • breast surgery
  • breast reconstruction
  • oncologic surgery

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Published Papers (1 paper)

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Research

15 pages, 208 KiB  
Article
Breaking Bones, Breaking Barriers: ChatGPT, DeepSeek, and Gemini in Hand Fracture Management
by Gianluca Marcaccini, Ishith Seth, Yi Xie, Pietro Susini, Mirco Pozzi, Roberto Cuomo and Warren M. Rozen
J. Clin. Med. 2025, 14(6), 1983; https://doi.org/10.3390/jcm14061983 - 14 Mar 2025
Viewed by 234
Abstract
Background: Hand fracture management requires precise diagnostic accuracy and complex decision-making. Advances in artificial intelligence (AI) suggest that large language models (LLMs) may assist or even rival traditional clinical approaches. This study evaluates the effectiveness of ChatGPT-4o, DeepSeek-V3, and Gemini 1.5 in [...] Read more.
Background: Hand fracture management requires precise diagnostic accuracy and complex decision-making. Advances in artificial intelligence (AI) suggest that large language models (LLMs) may assist or even rival traditional clinical approaches. This study evaluates the effectiveness of ChatGPT-4o, DeepSeek-V3, and Gemini 1.5 in diagnosing and recommending treatment strategies for hand fractures compared to experienced surgeons. Methods: A retrospective analysis of 58 anonymized hand fracture cases was conducted. Clinical details, including fracture site, displacement, and soft-tissue involvement, were provided to the AI models, which generated management plans. Their recommendations were compared to actual surgeon decisions, assessing accuracy, precision, recall, and F1 score. Results: ChatGPT-4o demonstrated the highest accuracy (98.28%) and recall (91.74%), effectively identifying most correct interventions but occasionally proposing extraneous options (precision 58.48%). DeepSeek-V3 showed moderate accuracy (63.79%), with balanced precision (61.17%) and recall (57.89%), sometimes omitting correct treatments. Gemini 1.5 performed poorly (accuracy 18.97%), with low precision and recall, indicating substantial limitations in clinical decision support. Conclusions: AI models can enhance clinical workflows, particularly in radiographic interpretation and triage, but their limitations highlight the irreplaceable role of human expertise in complex hand trauma management. ChatGPT-4o demonstrated promising accuracy but requires refinement. Ethical concerns regarding AI-driven medical decisions, including bias and transparency, must be addressed before widespread clinical implementation. Full article
(This article belongs to the Special Issue Artificial Intelligence, Computer Modeling and Technology: Clinical Changing Practice in Reconstructive Surgery)
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