The Current Landscape of Artificial Intelligence in Plastic Surgery Education and Training: A Systematic Review
Abstract
Objective Artificial intelligence (AI) shows promise in surgery, but its role in plastic surgery education remains underexplored. This review evaluates the current landscape of AI in plastic surgery education.Design A systematic search was conducted on August 11, 2024, across PubMed, CINAHL, IEEE, Scopus, Web of Science, and Google Scholar using terms related to AI, plastic surgery, and education. Original research articles focusing on AI in plastic surgery education were included, excluding correspondence, reviews, book chapters, theses, corrections, and non-peer-reviewed or non-English articles. Two investigators independently screened studies and synthesized data. ROBINS-I was used to assess bias.Results Fifteen studies were included, with 13 evaluating large language models (LLMs) such as ChatGPT, Microsoft Bing, and Google Bard. ChatGPT-4 outperformed other models on In-Service Examinations (average score of 72.7%) and demonstrated potential as a teaching assistant in plastic surgery education. AI-generated personal statements were comparable to human-written ones. However, ChatGPT showed inaccuracies in generating surgical protocols. ChatGPT demonstrated its ability to provide qualitative predictions, forecasting survey results that indicated limited current use of AI in plastic surgery education but support for further AI research. a study combined ChatGPT with DALL-E 2, a generative model, to create acceptable educational images. Machine learning was used in 1 study for evaluating surgical skill and providing real-time feedback during liposuction. Nine studies had low risk of bias, while 6 had moderate risk.Conclusions AI demonstrates potential as an educational tool in plastic surgery. However, limitations of evidence, such as AI model uncertainties, introduce ambiguity. While AI cannot replicate the expertise of seasoned surgeons, it shows promise for foundational learning and skill assessment. Developing authenticity guidelines and enhancing AI capabilities are essential for its effective, ethical integration into plastic surgery education.
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The code secure data handling reflects the growing institutional responsibility to protect user data as generative AI tools collect, process, and store information from students and staff.
“The code secure data handling reflects the growing institutional responsibility to protect user data as generative AI tools collect, process, and store information from students and staff (An et al., 2025; Barus et al., 2025; Genovese et al., 2025).”
From Six Institutional Intervention Areas to Support Ethical and Effective Student Use of Generative AI in Higher Education: A Narrative Review (2026) · cites this paper as Genovese et al., 2025
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- Authors
- Genovese, A., Borna, S., Gomez-Cabello, C. A., Haider, S. A., Prabha, S., Trabilsy, M., & Forte, A. J.
- Affiliations
- Mayo Clinic (US)
- Corresponding author
- Forte, Antonio Jorge
- ORCID
- Venue
- Journal of Surgical Education
- Publisher
- Elsevier BV
- Published
- 2025-08
- Volume
- 82
- Issue
- 8
- Pages
- 103519
- ISSN
- 1878-7452 (ISSN-L)
- 1931-7204
- OpenAlex
- W4410389768
- Cited by
- 21
- References
- 44
- Citations by year
- 2026: 15 · 2025: 6
- FWCI
- 1.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of surgical education (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Surgery › Surgical Simulation and Training
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- artificial intelligence, plastic surgery education, large language models, ChatGPT, surgical skill assessment, real-time feedback, generative AI, DALL-E 2, liposuction
- MeSH terms
- Artificial Intelligence, Clinical Competence, Education, Medical, Graduate, Humans, Surgery, Plastic
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