Didactic knowledge or Clinical Cases? How Data Types Shape Medical Large Language Models
Uncovering how textbook theory and clinical records uniquely shape medical diagnostic reasoning in AI models.
Medical large language models are commonly trained on mixtures of didactic data (e.g., textbooks) and clinical data (e.g., patient records), yet how these data types differentially shape model capabilities remains unclear. We address this issue with token-matched experiments that...
ArXiv CS.AI
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Understanding how distinct data sources influence LLM performance is critical for transitioning models from academic benchmarks to high-stakes clinical environments. This research highlights that the balance between textbook knowledge and patient-specific nuances determines whether an AI provides generalized theory or actionable diagnostic reasoning.
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