aceLarge Language Models and Applied Surgical Anatomy: Evaluation of Anatomical Reasoning in Expert-Derived Plastic Surgery Scenarios
Large language models (LLMs) are increasingly being integrated into medical education, research, and clinical decision-support applications; however, their ability to apply anatomical knowledge in clinically relevant surgical scenarios remains insufficiently studied. This study evaluated and compared the performance of six contemporary LLMs (ChatGPT, Gemini, Claude, DeepSeek, Copilot, and Manus) using 25 expert-derived applied surgical anatomy scenarios relevant to plastic surgery. Scenarios were developed by an…
On 25 expert-written plastic surgery anatomy scenarios, ChatGPT scored 250/250 and Gemini 248/250, with all six chatbots tested scoring at least 219/250.
Despite high scores, DeepSeek, Claude and Manus each made one critical error in a reconstructive vascular physiology scenario, showing anatomical misconceptions that could matter for surgical decisions.
Ceiling effects limited discrimination among top models, so the results reflect performance on this specific benchmark rather than a general ranking of anatomical reasoning ability.
Evidence
- Peer-reviewedClinical Anatomy2026-10-26
How should this claim be treated?
Truvace Impact Record TRV-2026-2289, v1: “Large Language Models and Applied Surgical Anatomy: Evaluation of Anatomical Reasoning in Expert-Derived Plastic Surgery Scenarios.” Truvace, 2026-10-27. /record/TRV-2026-2289 (accessed at citation time). sha256 166c98e4552b0457…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-2289 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.