AI-generated Clinical Histories for Radiology Reports: Closing the Information Gap

HomeRadiologyVol. 314, No. 2 PreviousNext Reviews and CommentaryEditorialAI-generated Clinical Histories for Radiology Reports: Closing the Information GapNeda Tavakoli1 , Daniel Kim1Neda Tavakoli1 , Daniel Kim1Author Affiliations1Department of Radiology, Northwestern University Feinberg School of Medicine, 737 N Michigan Ave, Ste 1600, Chicago, IL 60611Address correspondence to: N.T. (email: [email protected]).Neda Tavakoli1 Daniel Kim1Published Online:Feb 4 2025https://doi.org/10.1148/radiol.243910See also the article by Bhayana et al in this issue.MoreSectionsFull textPDF ToolsAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookXLinked In References1. Busby LP, Courtier JL, Glastonbury CM. Bias in radiology: the how and why of misses and misinterpretations. RadioGraphics 2018;38(1):236–247. Google Scholar2. Schön F, Sinzig R, Walther F, et al. Value of clinical information on radiology reports in oncological imaging. Diagnostics (Basel) 2022;12(7):1594. Medline Google Scholar3. Dang W, Stefanski PD, Kielar AZ, et al. Impact of clinical history on choice of abdominal/pelvic CT protocol in the emergency department. PLoS One 2018;13(8):e0201694. Medline Google Scholar4. Asgari E, Kaur J, Nuredini G, et al. Impact of electronic health record use on cognitive load and burnout among clinicians: narrative review. JMIR Med Inform 2024;12:e55499. Medline Google Scholar5. Elkassem AA, Smith AD. Potential use cases for ChatGPT in radiology reporting. AJR Am J Roentgenol 2023;221(3):373–376. Medline Google Scholar6. Bhayana R, Alwahbi O, Ladak AM, et al. Leveraging large language models to generate clinical histories for oncologic imaging requisitions. Radiology 2025;314(2):e242134. Google Scholar7. Yapp KE, Brennan P, Ekpo E. The effect of clinical history on diagnostic imaging interpretation – a systematic review. Acad Radiol 2022;29(2):255–266. Medline Google Scholar8. Walker CM, Abbott GF, Greene RE, Shepard JA, Vummidi D, Digumarthy SR. Imaging pulmonary infection: classic signs and patterns. AJR Am J Roentgenol 2014;202(3):479–492. Medline Google Scholar9. Ballard DH, Antigua-Made A, Barre E, et al. Impact of ChatGPT and large language models on radiology education: Association of Academic Radiology-Radiology Research Alliance Task Force white paper. Acad Radiol 2024. Google Scholar10. Zack T, Lehman E, Suzgun M, et al. Assessing the potential of GPT-4 to perpetuate racial and gender biases in health care: a model evaluation study. Lancet Digit Health 2024;6(1):e12–e22. [Published correction appears in Lancet Digit Health 2024;6(7):e445.] Medline Google ScholarArticle HistoryReceived: Dec 24 2024Revision requested: Jan 7 2025Revision received: Jan 8 2025Accepted: Jan 9 2025Published online: Feb 04 2025 FiguresReferencesRelatedDetailsAccompanying This ArticleLeveraging Large Language Models to Generate Clinical Histories for Oncologic Imaging RequisitionsFeb 4 2025Radiology Vol. 314, No. 2 Metrics Altmetric Score PDF download