arXiv:2508.16674v2 Announce Type: replace-cross Abstract: Medical report understanding from real-world document images is essential for generating patient-facing explanations and enabling structured information exchange in clinical systems. Existing VLMs and LLMs have shown strong performance on document understanding, but structured understanding of medical reports remains insufficiently benchmarked. Therefore, we introduce MedRepBench, a benchmark with 1,925 de-identified Chinese medical report images spanning diverse departments, patient demographics, and acquisition formats. In MedRepBench
The proliferation of advanced LLMs and VLMs has created a need for robust, specialized benchmarks to measure their effectiveness in high-stakes domains like medicine, where existing benchmarks are insufficient.
This benchmark is crucial for driving the development and validation of AI systems capable of accurately interpreting complex medical reports, a critical step towards their safe and effective deployment in healthcare.
The introduction of MedRepBench provides a standardized, challenging dataset for evaluating AI's ability to understand unstructured medical data, accelerating progress in clinical AI applications.
- · AI researchers and developers specializing in medical NLP
- · Healthcare providers adopting AI for administrative or clinical support
- · Patients benefiting from AI-assisted medical report interpretation
- · Biotech and MedTech companies
- · AI models that fail to perform adequately on specialized medical benchmarks
- · Manual data entry and interpretation services that lack AI augmentation
Improved performance of AI models in understanding and extracting information from medical reports.
Increased adoption of AI tools by healthcare systems for tasks like automating medical record analysis and generating patient summaries.
Potential for new clinical insights derived from large-scale, AI-powered analysis of de-identified medical report data, influencing treatment protocols.
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Read at arXiv cs.CL