@ShahidNShah
The proliferation of both general purpose and health care–specific large language models (LLMs) has intensified the challenge of effectively evaluating and comparing them. Data contamination plagues the validity of public benchmarks, self-preference distorts LLM-as-a-judge approaches, and there is a gap between the tasks used to test models and those used in clinical practice.
Large language models (LLMs) can significantly improve how patients are matched to suitable clinical trials by enhancing retrieval accuracy and handling complex eligibility criteria at scale. This AI-driven approach could streamline trial recruitment, reduce manual screening effort, and help more patients access appropriate research opportunities.
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