Didactic knowledge or Clinical Cases? How Data Types Shape Medical Large Language Models

Large language models (LLMs) designed for medicine are typically trained on a mix of didactic data—textbooks, reference materials—and clinical data such as patient records. Yet the distinct impact of each data type on model performance had not been clearly understood. In a recent study, researchers conducted controlled experiments in which they varied the ratio of didactic to clinical data while keeping the total token count constant. They then evaluated the models on tasks that required theoretical knowledge versus those demanding clinical reasoning. The findings revealed an asymmetric transfer: adding clinical data markedly improved performance on practice‑oriented tasks without harming the model’s ability to answer pure knowledge questions, whereas didactic data mainly boosted knowledge‑heavy tasks. Error analysis exposed a gap between knowing and doing, showing that greater recall does not automatically translate into better clinical reasoning. Moreover, the study found that only modest amounts of clinical data are needed to capture most of the benefits for electronic health record–based tasks, though the optimal proportion varies with the specific application. These results suggest that data selection for medical LLMs should be guided by intended use, prioritizing clinical information when advanced reasoning is required. Aligning data curation with clinical goals can yield safer, more effective AI tools for health professionals.

Read the original article on arXiv cs.AI

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