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This is a Experimental Study study.

October 18, 2025Open Access

MedKGEval: A Knowledge Graph-Based Multi-Turn Evaluation Framework for Open-Ended Patient Interactions with Clinical LLMs

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MedKGEval effectively identifies flaws in clinical interactions, enhancing LLM assessments during multi-turn dialogues.
Using a knowledge graph-driven mechanism, the framework ensures that patient simulations mimic realistic conversational behaviors.
In-situ evaluations allow real-time assessment of clinical appropriateness and factual correctness for each conversation turn.
The framework is adaptable for multiple languages, potentially broadening its application in diverse medical contexts.

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MedKGEval demonstrates improved evaluation of large language models in patient interactions, suggesting better clinical assessments.

Yu et al. (2025) studied this question.

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