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Grounding large language models in clinical diagnostics
Abstract Although Large Language Models (LLMs) possess extensive medical knowledge, they often struggle to emulate the complex, iterative process of real-world clinical diagnosis. To address this limitation, we present ClinDiag-GPT, a specialized LLM fine-tuned to execute full diagnostic procedures, supported by the ClinDiag-Framework evaluation system and ClinDiag-Benchmark, a dataset comprising 4,421 real-world cases.
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