The authors introduce a dataset of 2,000 real-world clinical check-up reports (with multimodal content like tables, images, and biomarkers) and ask models to generate structured "Action Cards" that tell patients what to do next—prioritized issues, which department to visit, timing, and patient-friendly explanations. They benchmark general-purpose and medical LLMs on this task, finding clear trade-offs between coverage (catching all issues), correctness, conciseness, and safety (not making diagnostic claims).
Main takeaways:
- Clinical check-up reports mix page layouts, tables, images, and domain jargon—hard for patients to interpret
- Action Cards structure the output: priority, department, timing, explanation, questions for clinicians, without diagnosing
- Dataset has 2,000 de-identified reports covering demographics, labs, imaging, and physician summaries
- Current LLMs show trade-offs: better issue coverage often hurts safety or conciseness