The Role of Artificial Intelligence in Plastic and Reconstructive Surgery: A Systematic Review of Clinical Applications, Accuracy, and Integration Challenges

Dandoulakis, Emmanouil (2025) The Role of Artificial Intelligence in Plastic and Reconstructive Surgery: A Systematic Review of Clinical Applications, Accuracy, and Integration Challenges. World Journal of Advanced Research and Reviews, 27 (2). pp. 1161-1179. ISSN 2581-9615

Background: Artificial intelligence (AI) is increasingly applied in medicine, including plastic and reconstructive surgery, to enhance diagnostic accuracy, surgical planning, outcome evaluation, and efficiency. However, integration into clinical practice remains limited. This systematic review assessed the current peer-reviewed clinical applications of AI across all plastic surgery subspecialties. Methods: Following PRISMA guidelines, we searched Medline, Embase, Cochrane, and PubMed for English-language studies (2015–2025) on AI in plastic/reconstructive surgery. Inclusion was limited to peer-reviewed clinical studies involving patients or patient data. Data on subspecialty, AI use-case, performance, and stage of development were extracted. Study quality was appraised with a modified MINORS tool. Results: The initial search yielded 2,153 records; 24 studies met all inclusion criteria. All major subspecialties were represented, especially aesthetic, breast and craniofacial. AI was applied across all subdisciplines, most commonly in aesthetic/cosmetic and craniofacial surgery. Key applications included image-based diagnostics, predictive analytics for surgical outcomes, augmented reality for surgical planning, and chatbot tools for patient education. Many algorithms achieved high accuracy or expert-level performance in research settings. However, the research was largely early-stage: most studies were retrospective and focused on model development (preclinical) with only one study demonstrating clinical implementation as of 2022. Quality appraisal showed that while nearly all studies had clearly stated aims and appropriate endpoints, only ~20% were prospective and only ~10–15% compared AI performance to current standards or clinicians. Overfitting was a concern, with just ~40% reporting use of validation techniques. Overall, included studies showed moderate methodological quality. Conclusions: AI applications in plastic surgery expanded substantially over the last decade, showing promise in improving diagnostic accuracy, surgical planning, and patient counseling. Nevertheless, most studies remain preliminary, with limited clinical translation to date. Stronger study designs – including prospective trials, external validation, and direct comparisons to standard care – are needed to establish the real-world efficacy of AI. Future research and clearer regulatory guidance are essential to safely integrate AI into routine plastic surgical practice.