Retrieval-Augmented Large Language Model for clinical decision support

Abstract

This study examines clinician interactions with a Knowledge Graph (KG)-enhanced Large Language Model (LLM) for diagnostic support, with an emphasis on the rare condition pseudohypoparathyroidism (PHP). Ten medical professionals engaged with simulated diagnostic scenarios, using the KG-enhanced LLM to support reasoning and validate differential diagnoses. Evaluation included basic model performance (RAGAS = 0.85; F1 = 0.79) and clinician-centered outcomes, such as diagnostic conclusions, confidence, adherence, and efficiency. Results show the tool was most valuable for rare or uncertain cases, increasing clinician confidence and supporting reasoning, while familiar cases elicited selective adoption with minimal AI engagement. Participant feedback indicated generally high usability, accuracy, and relevance, with most reporting improved efficiency and trust. Statistical analysis confirmed that AI assistance significantly reduced time-to-diagnosis (t(8) = 4.99, p = 0.001, Cohen’s dz = 1.66, 95% CI [73.8, 197.2]; Wilcoxon W = 0.0, p = 0.0039). These findings suggest that KG-enhanced LLMs can effectively augment clinician judgment in complex cases, serving as reasoning aids or educational tools while preserving clinician control over decision-making. The study emphasizes evaluating AI not only for accuracy, but also for practical utility and integration into real-world clinical workflows.

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