Large Language Models as an Inexpensive and Effective Extra Set of Eyes in Radiology Reporting

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  • 1. Brown LR. History of diagnostic radiology at the Mayo Clinic. AJR Am J Roentgenol 1993;161(6):1321–1325.
  • 2. Pezzullo JA, Tung GA, Rogg JM, Davis LM, Brody JM, Mayo-Smith WW. Voice recognition dictation: radiologist as transcriptionist. J Digit Imaging 2008;21(4):384–389.
  • 3. Bhayana R. Chatbots and Large Language Models in Radiology: A Practical Primer for Clinical and Research Applications. Radiology 2024;310(1):e232756.
  • 4. Brady AP. Error and discrepancy in radiology: inevitable or avoidable? Insights Imaging 2017;8(1):171–182.
  • 5. Gertz RJ, Dratsch T, Bunck AC, et al. Potential of GPT-4 for Detecting Errors in Radiology Reports: Implications for Reporting Accuracy. Radiology 2024;311(1):e232714.
  • 6. Hofvind S, Lee CI. Consensus Reads: The More Sets of Eyes Interpreting a Mammogram, the Better for Women. Radiology 2020;295(1):42–43.
  • 7. Rothenberg SA, Savage CH, Abou Elkassem A, et al. Prospective Evaluation of AI Triage of Pulmonary Emboli on CT Pulmonary Angiograms. Radiology 2023;309(1):e230702.
  • 8. Lång K, Josefsson V, Larsson AM, et al. Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study. Lancet Oncol 2023;24(8):936–944.

Article History

Received: Mar 20 2024
Revision requested: Mar 22 2024
Revision received: Mar 23 2024
Accepted: Mar 26 2024
Published online: Apr 16 2024