A Comparative Analysis of Large Language Models' Performance in the Context of Basal Cell Carcinoma

Abstract

Background: Large language models (LLMs) have been explored in various dermato-oncological conditions. In this study, we aimed to compare different LLMs' potential to guide clinicians on the treatment of basal cell carcinoma (BCC).

Method: Four authors formulated 24 questions on the topic of clinical management of BCC. The blinded responses of three LLMs (Gemini, Copilot and ChatGPT 4.0) were presented to a panel of nine dermato-oncologists for assessment of (i) factual accuracy, (ii) concision, (iii) comprehensiveness and (iv) overall preference. In addition, the responses were then quantitatively compared based on lexical (i.e., vocabulary) and semantic (i.e., meaning) similarity to three additional LLMs (ChatGPT 3.5, ChatGPT 4o and Claude). 

Results: ChatGPT 4.0 had the highest accuracy rate (87.5%, i.e., 21/24 responses), followed by Gemini (50%) and Copilot (25%). All models scored lower for concision and comprehensiveness, with ChatGPT 4.0 in the lead (62.5% comprehensive; 54.2% concise), followed by Gemini (33.3%; 12.5%) and Copilot (16.7%; 8.3%). The panel achieved consensus on model preference in 16 questions (ChatGPT 4.0: 54.2%; Gemini: 8.3%; Copilot: 4.2%; no consensus: 33.3%). While the lexical similarity was found to be low (x̄ ~0.07-0.10 across models), the semantic similarity between the LLM responses was moderate (x̄ ~0.60-0.70 across models). LLMs may assist clinicians in settings where expert dermato-oncological guidance is not readily available, with ChatGPT 4.0 currently outperforming both Gemini and Copilot. Since quantitative methods are unable to detect clinically relevant differences between LLMs, surveying dermatologists is necessary to identify useful models in this rapidly developing field.