Enhancing Clinical Decision Support and Differential Diagnosis with Knowledge Graphs, and Retrieval Augmented Generation in Generative AI

Diagnostic error carries a burden, while unconstrained large language models (LLMs) remain vulnerable to hallucination and weak integration of quantitative laboratory dynamics. We developed a decision-support pipeline combining disease-specific biomarker correlation graphs, ordinary differential equations (ODEs), deep sequence classification, and retrieval-augmented generation (RAG). For 103 disease classes from a full blood count (FBC) repository, biomarker networks were used as coupling matrices to generate 30 trajectories per disease (3,090 total). A one-dimensional convolutional neural network (CNN) and long short-term memory (LSTM) network classified disease trajectories and six dynamical clusters. A constrained GPT-4o-mini RAG layer used a 19-pattern BMJ Best Practice/NICE corpus to generate differential diagnoses evaluated for diagnostic suitability, evidential grounding, and clinical plausibility. Across five random-seed runs, disease-level accuracy was $0.940\pm 0.006$ for the CNN (95% CI 0.933–0.948) and $0.852\pm 0.019$ for the LSTM (95% CI 0.828–0.875); the CNN advantage was 8.87 percentage points (95% CI 6.47–11.27; $t(4)=10.26$, $p=5.1\times {10}^{-4}$; Hedges’ $g=3.67$). Among 100 sampled RAG cases, 96 parsed successfully; evidence was cited in 97.9%, the true diagnosis was mentioned in 71.9%, and the composite score was 3.82/5 with a 47.9% strict pass rate. The central finding was a decoupling between grounding and diagnostic correctness: classifier-correct versus classifier-wrong outputs differed in diagnostic suitability but not evidential grounding. Post-hoc analysis confirmed a 1.02-point diagnostic-score difference (Mann–Whitney $p=0.0024$; Hedges’ $g=0.72$), whereas grounding differed by only $-0.02$ points ($p=0.839$; $g=-0.04$).