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

Authors: Sike Xiang, Shuang Chen, Kevin Qinghong Lin et al.

arXiv · PDF

Summary

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

Relevance

Not related to my persona or midtraining work—this is a domain-specific medical benchmark for structured generation.

Threat model

Potential threat/caveat for clean result "Fine-tuning one persona on a two-marker chunk and another on the start marker plants the end marker at every donor answer's end, not chained to the start (LOW confidence)": this item discusses evaluation, benchmark.

Abstract

arXiv:2605.11533v1 Announce Type: new Abstract: Clinical check-up reports are multimodal documents that combine page layouts, tables, numerical biomarkers, abnormality flags, imaging findings, and domain-specific terminology. Such heterogeneous evidence is difficult for laypersons to interpret and translate into concrete follow-up actions. Although large language models show promise in medical summarisation and triage support, their ability to generate safe, prioritised, and patient-oriented actions from multimodal check-up reports remains under-benchmarked. We present \textbf{Checkup2Action}, a multimodal clinical check-up report dataset and benchmark for structured \textit{Action Card} generation. Each card describes one clinically relevant issue and specifies its priority, recommended department, follow-up time window, patient-facing explanation, and questions for clinicians, while avoiding diagnostic or treatment-prescriptive claims. The dataset contains 2,000 de-identified real-world check-up reports covering demographic information, physical examinations, laboratory tests, cardiovascular assessments, imaging-related evidence, and physician summaries. We formulate checkup-to-action generation as a constrained structured generation task and introduce an evaluation protocol covering issue coverage and precision, priority consistency, department and time recommendation accuracy, action complexity, usefulness, readability, and safety compliance. Experiments with general-purpose and medical large language models reveal clear trade-offs between issue coverage, action correctness, conciseness, and safety alignment. Checkup2Action provides a new multimodal benchmark for evaluating patient-oriented reasoning over clinical check-up reports.