Semantic Clinical Knowledge Augmentation Improves Medical Question Answering Across Commercial and Open Large Language Models.

OBJECTIVE

To evaluate whether semantic clinical knowledge augmentation through Semantic Clinical Artificial Intelligence (SCAI) improves medical question answering across commercial and open large language models, and to examine how a reasoning distilled model performs on the same benchmark.

MATERIALS AND

METHODS

We evaluated Google Gemini, Microsoft Copilot, Meta Llama 3 70B, and DeepSeek-R1-Distill-Llama-70B on official text only United States Medical Licensing Exam (USMLE) sample questions from Step 1 (n = 87), Step 2 CK (n = 103), and Step 3 (n = 123), with and without SCAI. SCAI comprised HD-NLP parsing, graph and knowledge graph embeddings, a trained SCAI LLM semantic knowledge reasoner, and a semantic knowledge base; the current implementation contained 33,023,902 triples across 246 relation labels. Responses were scored against the answer key with secondary human verification. All models produced a response to every text only item, so incorrect responses were counted as confabulations.

RESULTS

Across 313 items, accuracy increased from 90.7% to 97.1% for Gemini, 82.4% to 92.0% for Llama 3 70B, and 55.3% to 81.8% for DeepSeek-R1-Distill; Copilot changed from 93.6% to 94.2%. Within model gains were statistically significant for Gemini, Llama 3 70B, and DeepSeek-R1-Distill on all three steps, but not for Copilot. The best single model result was Llama 3 70B plus SCAI on Step 3 (121/123, 98.4%). Gemini plus SCAI was the numerically strongest commercial system.

CONCLUSIONS

SCAI was associated with materially better accuracy and lower confabulation (False Positive Results) across several heterogeneous LLMs, supporting a semantically grounded pipeline in which a dedicated SCAI LLM generates clinical context for a host LLM while suggesting that strong general reasoning performance does not automatically translate into healthcare readiness.