🤖 AI Summary
While current large language models demonstrate accuracy in clinical diagnosis, it remains unclear whether their reasoning follows stable, structured clinical logic. This work proposes the Clinical Reasoning Graph framework—a structured graph representation grounded in a clinical ontology comprising five node types and seven edge types—and leverages natural language processing and graph similarity metrics to extract and analyze 750 diagnostic trajectories. The study reveals that graph similarity between correct and incorrect diagnoses is nearly identical (0.488 vs. 0.484), and reasoning structures show no significant consistency across similar cases, indicating a lack of schematic-level stability in cross-case reasoning. Although structured reflection prompts improve feature analysis, they do not enhance structural consistency. These findings underscore the need for process-level evaluation to complement conventional outcome-based accuracy and offer a novel paradigm for explainability in clinical AI.
📝 Abstract
Modern large language models (LLMs) reach 60-70% diagnostic accuracy on complex clinical case benchmarks, but accuracy alone cannot distinguish stable clinically-grounded reasoning from pattern matching. We introduce clinical reasoning graphs, structured graph representations extracted from free-text LLM diagnostic traces using a domain-grounded ontology with 5 node types and 7 edge types. We apply this pipeline to 750 traces from five LLMs across 50 New England Journal of Medicine Clinicopathological Conference cases and three prompt conditions, and test whether diagnostic traces show stable structured reasoning patterns, or diagnostic schemas, for clinically similar cases. We operationalize this as higher graph similarity among clinically similar cases than among clinically dissimilar ones. Across 15 model-condition comparisons, within-cluster and between-cluster composite similarity are nearly equal, and no comparison survives multiple-testing correction; a component-level analysis finds any residual content signal far below schema scale. Graph similarity is also nearly identical for pairs of models that are both correct (0.488) and both incorrect (0.484), suggesting that graph structure captures a dimension not reflected in diagnostic accuracy. Structured reflection prompting increases explicit discriminating-feature analysis within traces (+33%) but does not increase cross-case consistency. These results show diagnostic competence without schema-scale reasoning consistency, and indicate that final-answer accuracy should be complemented by process-level evaluation. We release the ontology, extraction pipeline, validation protocol, and the extracted reasoning graphs and similarity artifacts as resources for structured evaluation of LLM clinical reasoning.