Checkup2Action: A Multimodal Clinical Check-up Report Dataset for Patient-Oriented Action Card Generation

Abstract:Routine clinical check-up reports combine laboratory measurements, physiological assessments, imaging findings and visually structured information, but rarely tell patients what to do next. Translating them into follow-up actions requires models to connect evidence across pages, tables and modalities, identify clinically relevant issues and communicate next steps without unsupported diagnostic or treatment claims. Yet this report-to-action capability remains poorly benchmarked. We introduce C2A, a dataset and benchmark for generating structured \textit{Action Cards} from multimodal check-up reports, together with Checkup2Action, a constrained workflow for the task. C2A contains 2,000 de-identified real-world reports covering physical examinations, laboratory tests, cardiovascular assessments and imaging evidence. Each card specifies one issue, its priority, recommended department, follow-up window, patient-facing explanation and questions for clinicians. Our evaluation measures issue coverage and precision, priority consistency, department and timing accuracy, action complexity, usefulness, readability and safety. Experiments across general-purpose and medical large language models show that no model performs best on every dimension. Clinical experts judged 84% of evaluated outputs fully reasonable and effective. Removing safety constraints increased problem recall from 0.527 to 0.825, but produced more diagnostic overstatement. C2A therefore supports systematic study of the balance between coverage, actionable guidance and safe patient communication.

Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2605.11533 [cs.CL]

(or arXiv:2605.11533v4 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.11533 Focus to learn more arXiv-issued DOI via DataCite

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