CaseReportBench: An LLM Benchmark Dataset for Dense Information Extraction in Clinical Case Reports

• URL: http://arxiv.org/abs/2505.17265v1
• Date: Thu, 22 May 2025 20:21:32 GMT
• Title: CaseReportBench: An LLM Benchmark Dataset for Dense Information Extraction in Clinical Case Reports
• Authors: Xiao Yu Cindy Zhang, Carlos R. Ferreira, Francis Rossignol, Raymond T. Ng, Wyeth Wasserman, Jian Zhu,
• Abstract summary: We introduce CaseReportBench, an expert-annotated dataset for dense information extraction of case reports, focusing on IEMs.<n>We assess various models and prompting strategies, introducing novel approaches such as category-specific prompting and subheading-filtered data integration.<n>Our clinician evaluations show that LLMs can extract clinically relevant details from case reports, supporting rare disease diagnosis and management.
• Score: 4.477840500181267
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Rare diseases, including Inborn Errors of Metabolism (IEM), pose significant diagnostic challenges. Case reports serve as key but computationally underutilized resources to inform diagnosis. Clinical dense information extraction refers to organizing medical information into structured predefined categories. Large Language Models (LLMs) may enable scalable information extraction from case reports but are rarely evaluated for this task. We introduce CaseReportBench, an expert-annotated dataset for dense information extraction of case reports, focusing on IEMs. Using this dataset, we assess various models and prompting strategies, introducing novel approaches such as category-specific prompting and subheading-filtered data integration. Zero-shot chain-of-thought prompting offers little advantage over standard zero-shot prompting. Category-specific prompting improves alignment with the benchmark. The open-source model Qwen2.5-7B outperforms GPT-4o for this task. Our clinician evaluations show that LLMs can extract clinically relevant details from case reports, supporting rare disease diagnosis and management. We also highlight areas for improvement, such as LLMs' limitations in recognizing negative findings important for differential diagnosis. This work advances LLM-driven clinical natural language processing and paves the way for scalable medical AI applications.

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