ArticleJournal of the American College of Radiology : JACR2025
RadGPT: A System Based on a Large Language Model That Generates Sets of Patient-Centered Materials to Explain Radiology Report Information.
Article in Journal of the American College of Radiology : JACR, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Large language models for simplifying radiology reports: a systematic review and meta-analysis of patient, public, and clinician evaluations.The Lancet. Digital health · 2026Pooled it
- How do non-radiologist physicians read and use the radiology report? A French national survey.Insights into imaging · 2026Article
- Assessing family engagement and acceptance of patient-friendly radiology reports at two academic children's hospitals.Pediatric radiology · 2026Article
- Patient understanding of AI-simplified oncology imaging reports requires further validation.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Patients prefer ChatGPT to institutional websites for questions on radiation-based imaging exams: international mixed-methods study.Radiology advances · 2026Article
- Benchmarking Multimodal Vision Frontier Models With Lumbar Spine MRIs for Grading Lumbar Spinal Stenosis.Global spine journal · 2026Article
- Improving patient understanding of oncology imaging: radiologist and patient evaluation of summarised versus full-length AI-simplified reports from a tertiary cancer centre.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Multidimensional evaluation of large language models in radiology report readability.NPJ digital medicine · 2026Article
- Large Language Models in Radiologist-Patient Communication: A Narrative Review for Clinical Practice.Cureus · 2026Review
- The Effect of AI on the Radiologist Workforce: A Task-Based Analysis.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
purposeThe 21st Century Cures Act final rule requires that patients have real-time access to their radiology reports, which contain technical language. The objective of this study to was to use a novel system called RadGPT, which integrates concept extraction and a large language model (LLM), to help patients understand their radiology reports.
methodsRadGPT generated 150 concept explanations and 390 question-and-answer pairs from 30 radiology report impressions from between 2012 and 2020. The extracted concepts were used to create concept-based explanations, as well as concept-based question-and-answer pairs for which questions were generated using either a fixed template or an LLM. Additionally, report-based question-and-answer pairs were generated directly from the impression using an LLM without concept extraction. One board-certified radiologist and four radiology residents rated the material quality using a standardized rubric.
resultsConcept-based LLM-generated questions were of significantly higher quality than concept-based template-generated questions (P < .001). Excluding those template-based question-and-answer pairs from further analysis, nearly all (>95%) of RadGPT-generated materials were rated highly, with at least 50% receiving the highest possible ranking from all five raters. No answers or explanations were rated as likely to affect the safety or effectiveness of patient care. Report-level LLM-based questions and answers were rated particularly highly, with 92% of report-level LLM-based questions and 61% of the corresponding report-level answers receiving the highest rating from all raters.
conclusionsThe educational tool RadGPT generated high-quality explanations and question-and-answer pairs that were personalized for each radiology report, unlikely to produce harmful explanations, and likely to enhance patient understanding of radiology information.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.