New benchmark MedicalBench evaluates LLMs on implicit medical concept extraction

Researchers have introduced MedicalBench, a new benchmark designed to evaluate the ability of large language models (LLMs) to extract medical concepts from electronic health records, particularly focusing on implicitly stated information. The benchmark, built using MIMIC-IV discharge summaries and ICD-10 codes, includes complex cases such as implicit positives and semantically confusable negatives. Initial evaluations show that current state-of-the-art LLMs perform modestly on MedicalBench, indicating a significant challenge in extracting implicitly expressed medical concepts and justifying their predictions. AI

IMPACT This benchmark could drive development of more capable LLMs for the healthcare industry, improving accuracy and interpretability in medical data analysis.

RANK_REASON The item describes a new academic benchmark for evaluating LLMs on a specific task (medical concept extraction), including a published paper on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• Gotit.pub
• Hugging Face
• ICD-10
• MedicalBench
• MIMIC-IV
• Sanjit Singh Batra

• paper
• other

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