Human vs Machine: The Future of Decision-making in Plastic and Reconstructive Surgery.

Abstract

Background: Artificial intelligence-driven technologies offer transformative potential in plastic surgery, spanning preoperative planning, surgical procedures, and postoperative care, with the promise of improved patient outcomes.

Objectives: To compare the web-based ChatGPT-4o (omni; OpenAI, San Francisco, CA) and Gemini Advanced (Alphabet Inc., Mountain View, CA), focusing on their data upload feature and examining outcomes before and after exposure to continuing medical education (CME) articles, particularly regarding their efficacy relative to human participants.

Methods: Participants and large language models (LLMs) completed 22 multiple-choice questions to assess baseline knowledge of CME topics. Initially, both LLMs and participants answered without article access. In incognito mode, the LLMs repeated the tests over 6 days. After accessing the articles, responses from both LLMs and participants were extracted and analyzed.

Results: There was a significant increase in mean scores after the article was read in the resident group, indicating a significant rise. In the LLM groups, the ChatGPT-4o (omni) group showed no significant difference between pre- and postarticle scores, but the Gemini Advanced group demonstrated a significant increase. It can be stated that the ChatGPT-4o and Gemini Advanced groups have higher accuracy means compared with the resident group in both pre- and postarticle periods.

Conclusions: The analysis between human participants and LLMs indicates promising implications for the incorporation of LLMs in medical education. Because these models increase in sophistication, they offer the potential to serve as supplementary tools within traditional learning environments. This could aid in bridging the gap between theoretical knowledge and practical implementation.