Benchmarking large language models for drug combination alerts: achieving expert-level reliability via knowledge grounding and contextual reasoning
Abstract
This study systematically evaluated the potential of LLMs for drug combination alerting, focusing on four key aspects: (1) the baseline performance of native LLMs, (2) the contribution of external knowledge grounding via Retrieval-Augmented Generation (RAG), (3) the impact of expert-guided reasoning using context engineering, and (4) the utility of a multi-agent architecture for comprehensive, interpretable risk analysis. The source code used to conduct all analyses in this study has been deposited in a public GitHub repository (https://github.com/studentiz/comed) and is available as a Python package (https://pypi.org/project/comed/).
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