The Reliability of LLMs for Medical Diagnosis: An Examination of Consistency, Manipulation, and Contextual Awareness

This study evaluated the diagnostic reliability of two Large Language Models (LLMs), Google Gemini 2.0 Flash and OpenAI ChatGPT-4o, across three dimensions: consistency under rephrased inputs, susceptibility to irrelevant prompt content, and responsiveness to added clinical context. We designed 52 clinical scenarios and modified each under controlled conditions. For consistency, scenarios were rephrased with demographic, wording, and examination changes that preserved the diagnostic core. And the susceptibility was evaluated through embedding irrelevant but plausible narrative details while keeping the clinical evidence unchanged. For contextual awareness, patient history, lifestyle data, or diagnostic findings were added to shift the expected diagnosis. Physician reviewers then judged whether context-driven changes were clinically appropriate. Both models returned identical diagnoses across all equivalent variants and repeated queries (100% consistency). When irrelevant details were added, Gemini changed its diagnosis in 40.0% of cases and ChatGPT in 30.0%. ChatGPT responded to context more often than Gemini (77.8% vs. 55.6%), but a larger share of its changes were clinically inappropriate (33.3% vs. 22.2%). Gemini's context-driven changes were more often judged appropriate (66.7% vs. 55.6%). Consistency under controlled inputs did not protect either model from irrelevant manipulation or unjustified diagnostic shifts when context changed. Before LLMs can separate relevant from irrelevant input and flag insufficient evidence, their diagnostic use requires clinician oversight and structured safeguards.