RAD-CaseBookLLM-08: An open-access dataset of structured large language model–generated radiology differential diagnosis teachings

Abstract

<ns7:p>Background Large language models are increasingly explored in medical education, particularly for generating structured explanatory content. However, openly accessible datasets capturing full-length model outputs in a standardized and reusable format remain limited. In radiology education, differential diagnosis teaching is typically organized around key imaging findings integrated with clinical reasoning. We developed RAD-CaseBookLLM-08, an open dataset of large language model–generated radiology differential diagnosis teachings derived from lesion-based thematic topics. Methods The dataset comprises 225 cases across nine radiology subspecialties. Thematic key imaging findings were derived from an established case-based radiology textbook and used as structured prompts. All cases were generated using ChatGPT-4o (OpenAI) in March 2025 via a web-based interface with conversation memory disabled. Each topic was processed in an independent session using an identical prompt template in which only the subspecialty and imaging finding were modified. Outputs were copied verbatim without editing, correction, or validation, and formatting elements were preserved. The dataset is provided in Microsoft Word and Portable Document Format files and is organized by subspecialty with sequential case labeling. No patient data were included. Conclusions RAD-CaseBookLLM-08 provides a structured, time-stamped collection of large language model–generated radiology teaching texts. The dataset may support reproducibility studies, benchmarking of model outputs, prompt engineering evaluation, and analysis of educational structure in machine-generated differential diagnoses. It is openly available under a Creative Commons Zero license via Zenodo.</ns7:p>

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