High Consistency, Limited Accuracy: Evaluating Large Language Models for Binary Medical Diagnosis

BackgroundLarge Language Models (LLMs) have demonstrated impressive capabilities in medical knowledge tasks, achieving 60-80% accuracy on licensing examinations. However, their reliability and consistency in clinical diagnosis--critical for clinical trustworthiness-remain incompletely characterized. ObjectiveTo systematically evaluate the consistency and diagnostic accuracy of state-of-the-art LLMs in binary medical diagnosis, examining the relationship between reproducibility and diagnostic performance. MethodsWe evaluated three frontier LLMs (GPT-40, Gemini-2.0-Flash, Qwen-Plus) on heart disease diagnosis using 100 diverse clinical cases from the UCI Heart Disease dataset. Each model performed 4 independent assessments per case (1,200 total predictions). We tested two prompt variations ("Expert Cardiologist" vs "Neutral Assessor") and measured intra-model consistency, inter-model agreement, diagnostic accuracy, and prompt sensitivity using a SQLite-based checkpoint system. ResultsAll models achieved exceptional intra-model consistency (99-100%), with Qwen-Plus demonstrating perfect reproducibility (100%). Inter-model agreement was similarly high (98-99%), indicating convergent reasoning patterns. However, diagnostic accuracy remained at approximately 50%, equivalent to random guessing. Models exhibited strong systematic bias toward positive diagnosis (49-51 false positives vs 0-1 false negatives per 100 cases). Prompt variation had minimal impact ([≤]3% prediction changes), and error patterns were highly systematic, with all models making identical errors on 48-51% of cases. This created a consistency-accuracy gap of approximately 50 percentage points. ConclusionsOur findings reveal a critical dissociation between consistency and accuracy in LLM medical diagnosis. While LLMs demonstrate remarkable reproducibility-a desirable property for clinical tools-their systematic tendency toward over-diagnosis and limited discriminative accuracy constrain direct clinical utility. The high inter-model agreement on errors suggests fundamental limitations in applying general-purpose LLMs to medical diagnosis rather than model-specific artifacts. Results suggest LLMs may be better suited as supplementary decision-support tools with human oversight rather than primary diagnostic systems. Future development should prioritize discriminative fine-tuning on labeled diagnostic datasets and calibration techniques to address systematic biases.