A Comparative Assessment of ChatGPT, Gemini, and DeepSeek Accuracy: Examining Visual Medical Assessment in Internal Medicine Cases with and Without Clinical Context.

ABSTRACT: Background and Aim: Large language models (LLMs) demonstrate significant potential in assisting with medical image interpretation. However, the diagnostic accuracy of general-purpose LLMs on image-based internal medicine cases and the added value of brief clinical history remain unclear. This study evaluated three general-purpose LLMs (ChatGPT, Gemini, and DeepSeek) on expert-curated cases to quantify diagnostic accuracy with image-only input versus image plus brief clinical context. Methods: We conducted a comparative evaluation using 138 expert-curated cases from Harrison's Visual Case Challenge. Each case was presented to the models in two distinct phases: Phase 1 (image only) and Phase 2 (image plus a brief clinical history). The primary endpoint was top-1 diagnostic accu

SUBMITTER: Asiri R 

PROVIDER: S-EPMC12897363 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature