aceComparative Evaluation of Large Language Models in the Recognition of Postoperative Complications Following Aesthetic Surgery
Background: Large language models (LLMs) are increasingly being explored as supportive tools in postoperative patient communication. However, their ability to recognize and appropriately manage postoperative surgical complications remains unclear. This study comparatively evaluated ChatGPT, Gemini, and Claude in postoperative complication assessment following aesthetic surgical procedures. Methods: Thirty standardized postoperative clinical scenarios representing low-, moderate-, and high-risk complications were…
In 30 standardized aesthetic-surgery scenarios, three LLMs showed moderate-to-high ability to recognize common postoperative complications, with diagnostic accuracy means near 3.6 to 4.0 on a 5-point scale.
Performance varied in clinically urgent scenarios: Gemini recommended appropriate emergency referral in only 76.7% of high-risk evaluations, and it had the most critical management errors.
The study used 30 standardized scenarios rather than real patient interactions, and the authors note variability became more apparent in clinically urgent cases, so the tools cannot replace physician assessment.
Evidence
- Peer-reviewedAesthetic Plastic Surgery2026-10-14
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Truvace Impact Record TRV-2026-1453, v1: “Comparative Evaluation of Large Language Models in the Recognition of Postoperative Complications Following Aesthetic Surgery.” Truvace, 2026-10-15. /record/TRV-2026-1453 (accessed at citation time). sha256 94587b081c504eb4…
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