December 2024
Plastic surgery has recently seen a significant rise in the adoption of artificial intelligence (AI) and machine learning (ML), aligning with broader trends across the medical field.
Why this matters
Plastic surgery has recently seen a significant rise in the adoption of artificial intelligence (AI) and machine learning (ML), aligning with broader trends across the medical field. This work contributes to evidence-based plastic and reconstructive surgery — helping clinicians interpret outcomes, refine technique, and counsel patients with clearer data.
Author
Plastic and reconstructive surgery resident at Emory University (UCLA BS, USC MD) with an h-index of 21 and peer-reviewed work spanning aesthetic surgery, reconstruction, medical devices, and AI in medicine.
Abstract
Plastic surgery has recently seen a significant rise in the adoption of artificial intelligence (AI) and machine learning (ML), aligning with broader trends across the medical field. These technologies aim to enhance surgical efficiency, optimize treatment planning, predict post-surgical aesthetic results, streamline patient management, and improve overall surgical decision-making. However, the integration of AI/ML into clinical practice presents ethical and practical challenges, including concerns about transparency, data bias, and security. The regulation and approval of AI/ML technologies, much like other medical devices, remain complex and continuously evolving. The US FDA has been proactive in addressing these challenges, developing specific frameworks for AI- and/or ML-based devices.