Yamin MA, Raquepo TM, Tobin M, Posso AN, Cauley RP. Applications and challenges of artificial intelligence in plastic surgery imaging: A narrative review. Artif Intell Med Imaging 2025; 6(2): 108028 [DOI: 10.35711/aimi.v6.i2.108028]
Corresponding Author of This Article
Ryan P Cauley, MD, Assistant Professor, FACS, Division of Plastic and Reconstructive Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02115, United States. rcauley@bidmc.harvard.edu
Research Domain of This Article
Surgery
Article-Type of This Article
Systematic Reviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Artif Intell Med Imaging. Sep 8, 2025; 6(2): 108028 Published online Sep 8, 2025. doi: 10.35711/aimi.v6.i2.108028
Applications and challenges of artificial intelligence in plastic surgery imaging: A narrative review
Mohammed Ahmed Yamin, Tricia Mae Raquepo, Micaela Tobin, Agustin N Posso, Ryan P Cauley
Mohammed Ahmed Yamin, Tricia Mae Raquepo, Micaela Tobin, Agustin N Posso, Division of Plastic and Reconstructive Surgery, Beth Israel Deaconess Medical Center, Boston, MA 02115, United States
Ryan P Cauley, Division of Plastic and Reconstructive Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02115, United States
Co-first authors: Mohammed Ahmed Yamin and Tricia Mae Raquepo.
Author contributions: Yamin M and Raquepo TM were responsible for study conception, literature review, data collection, analysis, and manuscript writing; Tobin MJ contributed to manuscript revision and editing; Cauley RP supervised the study, provided guidance on methodology, and contributed to manuscript finalization; All authors reviewed and approved the final manuscript.
Conflict-of-interest statement: No conflict-of-interest to be reported for any of the authors.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Ryan P Cauley, MD, Assistant Professor, FACS, Division of Plastic and Reconstructive Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02115, United States. rcauley@bidmc.harvard.edu
Received: April 3, 2025 Revised: May 25, 2025 Accepted: August 12, 2025 Published online: September 8, 2025 Processing time: 153 Days and 8.9 Hours
Abstract
BACKGROUND
As artificial intelligence (AI) continues to expand across medical specialties, its application in medical imaging within plastic and reconstructive surgery (PRS) remains limited in the literature. Imaging plays a critical role in surgical planning, intraoperative decision-making, and postoperative monitoring in PRS, presenting an opportunity for AI to enhance clinical outcomes.
AIM
To evaluate the current applications of AI in medical imaging for plastic surgery, with a focus on its use in preoperative planning, intraoperative guidance, and postoperative monitoring.
METHODS
A literature search was conducted using MEDLINE, EMBASE, ScienceDirect, and OVID up to February 2025. Studies were included based on relevance to AI use in plastic surgery imaging. Extracted data included AI modality, surgical context, outcomes, and limitations. The search followed PRISMA guidelines and was registered with PROSPERO (CRD420251008741).
RESULTS
AI tools have improved preoperative planning through three-dimensional vascular mapping, augmented reality, and thermographic imaging. Intraoperatively, AI-enabled navigation and robotic systems have increased surgical precision. Postoperative AI applications, including deep learning algorithms and sensor-based monitoring, support early complication detection and wound healing assessment. However, persistent barriers include data variability, model generalizability, surgeon unfamiliarity, and lack of regulatory standards.
CONCLUSION
AI-driven imaging technologies show promise in enhancing decision-making and outcomes in PRS. To ensure safe clinical integration, future efforts must focus on structured validation, standardization, and ethical oversight.
Core Tip: This narrative review demonstrates the promise of artificial intelligence (AI) applications in medical imaging for plastic and reconstructive surgery, such as preoperative planning with augmented reality, intraoperative surgical guidance, and deep learning for detecting postoperative complications. However, concerns remain regarding regulatory processes, AI bias, and data standardization. Understanding AI-driven imaging technologies will be crucial for safe clinical implementation.
