Do Current Large Language Models Master Adequate Clinical Knowledge?
Primary Area: generative models
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Keywords: Large Language Model, Medical Large Language Model, Clinical Knowledge, Knowledge Graph
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Abstract: Large Language Models (LLMs) show promising potential in solving clinical problems. Current LLMs, including so-called medical LLMs, are reported to achieve excellent performance on certain medical evaluation benchmarks, such as medical question answering, medical exams, etc. However, such evaluations cannot assess whether LLMs have mastered sufficient, compressive, and necessary medical knowledge for solving real clinical problems, such as clinical diagnostic assistance. In this paper, we propose a framework to assess the mastery of LLMs in clinical knowledge. Firstly, we construct a large medical disease-based knowledge base, MedDisK, covering 10,632 common diseases across 18 clinical knowledge aspects, which are crucial for diagnosing and treating diseases. Built on that, we propose a MedDisK-based evaluation method MedDisKEval: We prompt LLMs to retrieve information related to these clinical knowledge aspects. Then, we evaluate an LLM's mastery of medical knowledge by measuring the similarity between the LLM-generated information and the content within our knowledge base. Our experimental findings reveal that over 50\% of the clinical information generated by our evaluated LLMs is significantly inconsistent with the corresponding knowledge stored in our knowledge base. We further perform a significance analysis to compare the performance of medical LLMs with their backbone models, discovering that 5 out of 6 medical LLMs perform less effectively than their backbone models in over half of the clinical knowledge aspects. These observations demonstrate that existing LLMs have not mastered adequate knowledge for clinical practice. Our findings offer novel and constructive insights for the advancement of medical LLMs.
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Submission Number: 9466
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