jcm-logo

Journal Browser

Journal Browser

Integrating Artificial Intelligence into Plastic, Reconstructive, and Aesthetic Surgery: Innovations, Challenges, and Clinical Application

Special Issue Editors


E-Mail Website
Guest Editor

E-Mail
Guest Editor Assistant
Plastic Surgery Unit, Department of Medicine, Surgery and Neuroscience, University of Siena, 53100 Siena, Italy
Interests: plastic; reconstructive; aesthetic surgery; artificial intelligence; AI in plastic surgery

Special Issue Information

Dear Colleagues,

In recent years, artificial intelligence has begun to transform plastic, reconstructive, and aesthetic surgery, opening new possibilities in preoperative planning, outcome prediction, wound management, and the objective evaluation of results. This Special Issue will gather contributions exploring how AI models such as machine learning, deep learning, multimodal, and generative systems can be integrated into clinical and experimental practice. Topics include surgical decision support, automated image and data analysis, preoperative simulation, complication prediction, and postoperative optimization. Practical aspects of transparency, privacy, and model reproducibility will also be considered. Our goal is to provide a broad and updated overview of current innovations, key challenges, and future clinical translation for AI in plastic surgery.

Prof. Dr. Roberto Cuomo
Guest Editor

Dr. Gianluca Marcaccini
Guest Editor Assistant

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. 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

  • plastic
  • reconstructive
  • aesthetic surgery
  • artificial intelligence
  • AI in plastic surgery

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

21 pages, 9141 KB  
Article
AI vs. MD: Benchmarking ChatGPT and Gemini for Complex Wound Management
by Luca Corradini, Gianluca Marcaccini, Ishith Seth, Warren M. Rozen, Camilla Biagiotti, Roberto Cuomo and Francesco Ruben Giardino
J. Clin. Med. 2025, 14(24), 8825; https://doi.org/10.3390/jcm14248825 - 13 Dec 2025
Viewed by 1084
Abstract
Background: The management of hard-to-heal wounds poses a major clinical challenge due to heterogeneous etiology and significant global healthcare costs (estimated at USD 148.64 billion in 2022). Large Language Models (LLMs), such as ChatGPT and Gemini, are emerging as potential decision-support tools. This [...] Read more.
Background: The management of hard-to-heal wounds poses a major clinical challenge due to heterogeneous etiology and significant global healthcare costs (estimated at USD 148.64 billion in 2022). Large Language Models (LLMs), such as ChatGPT and Gemini, are emerging as potential decision-support tools. This study aimed to rigorously assess the accuracy and reliability of ChatGPT and Gemini in the visual description and initial therapeutic management of complex wounds based solely on clinical images. Methods: Twenty clinical images of complex wounds from diverse etiologies were independently analyzed by ChatGPT (version dated 15 October 2025) and Gemini (version dated 15 October 2025). The models were queried using two standardized, concise prompts. The AI responses were compared against a clinical gold standard established by the unanimous consensus of an expert panel of three plastic surgeons. Results: Statistical analysis showed no significant difference in overall performance between the two models and the expert consensus. Gemini achieved a slightly higher percentage of perfect agreement in management recommendations (75.0% vs. 60.0% for ChatGPT). Both LLMs demonstrated high proficiency in identifying the etiology of vascular lesions and recognizing critical “red flags,” such as signs of ischemia requiring urgent vascular assessment. Noted divergences included Gemini’s greater suspicion of potential neoplastic etiology and the models’ shared error in suggesting Negative Pressure Wound Therapy (NPWT) in a case potentially contraindicated by severe infection. Conclusions: LLMs, particularly ChatGPT and Gemini, demonstrate significant potential as decision-support systems and educational tools in wound care, offering rapid diagnosis and standardized initial management, especially in non-specialist settings. Instances of divergence in systemic treatments or in atypical presentations highlight the limitations of relying on image-based reasoning alone. Ultimately, LLMs serve as powerful, scalable assets that, under professional supervision, can enhance diagnostic speed and improve care pathways. Full article
Show Figures

Figure 1

Back to TopTop