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Gertz RJ, Dratsch T, Bunck AC, Lennartz S, Iuga AI, Hellmich MG, Persigehl T, Pennig L, Gietzen CH, Fervers P, Maintz D, Hahnfeldt R, Kottlors J. Potential of GPT-4 for Detecting Errors in Radiology Reports: Implications for Reporting Accuracy. Radiology 2024; 311:e232714. [PMID: 38625012 DOI: 10.1148/radiol.232714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
Abstract
Background Errors in radiology reports may occur because of resident-to-attending discrepancies, speech recognition inaccuracies, and large workload. Large language models, such as GPT-4 (ChatGPT; OpenAI), may assist in generating reports. Purpose To assess effectiveness of GPT-4 in identifying common errors in radiology reports, focusing on performance, time, and cost-efficiency. Materials and Methods In this retrospective study, 200 radiology reports (radiography and cross-sectional imaging [CT and MRI]) were compiled between June 2023 and December 2023 at one institution. There were 150 errors from five common error categories (omission, insertion, spelling, side confusion, and other) intentionally inserted into 100 of the reports and used as the reference standard. Six radiologists (two senior radiologists, two attending physicians, and two residents) and GPT-4 were tasked with detecting these errors. Overall error detection performance, error detection in the five error categories, and reading time were assessed using Wald χ2 tests and paired-sample t tests. Results GPT-4 (detection rate, 82.7%;124 of 150; 95% CI: 75.8, 87.9) matched the average detection performance of radiologists independent of their experience (senior radiologists, 89.3% [134 of 150; 95% CI: 83.4, 93.3]; attending physicians, 80.0% [120 of 150; 95% CI: 72.9, 85.6]; residents, 80.0% [120 of 150; 95% CI: 72.9, 85.6]; P value range, .522-.99). One senior radiologist outperformed GPT-4 (detection rate, 94.7%; 142 of 150; 95% CI: 89.8, 97.3; P = .006). GPT-4 required less processing time per radiology report than the fastest human reader in the study (mean reading time, 3.5 seconds ± 0.5 [SD] vs 25.1 seconds ± 20.1, respectively; P < .001; Cohen d = -1.08). The use of GPT-4 resulted in lower mean correction cost per report than the most cost-efficient radiologist ($0.03 ± 0.01 vs $0.42 ± 0.41; P < .001; Cohen d = -1.12). Conclusion The radiology report error detection rate of GPT-4 was comparable with that of radiologists, potentially reducing work hours and cost. © RSNA, 2024 See also the editorial by Forman in this issue.
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Affiliation(s)
- Roman Johannes Gertz
- From the Institute of Diagnostic and Interventional Radiology (R.J.G., T.D., A.C.B., S.L., A.I.I., T.P., L.P., C.H.G., P.F., D.M., R.H., J.K.) and Institute of Medical Statistics and Bioinformatics (M.G.H.), Faculty of Medicine, University Hospital Cologne, University of Cologne, Kerpener Strasse 62, 50937 Cologne, Germany
| | - Thomas Dratsch
- From the Institute of Diagnostic and Interventional Radiology (R.J.G., T.D., A.C.B., S.L., A.I.I., T.P., L.P., C.H.G., P.F., D.M., R.H., J.K.) and Institute of Medical Statistics and Bioinformatics (M.G.H.), Faculty of Medicine, University Hospital Cologne, University of Cologne, Kerpener Strasse 62, 50937 Cologne, Germany
| | - Alexander Christian Bunck
- From the Institute of Diagnostic and Interventional Radiology (R.J.G., T.D., A.C.B., S.L., A.I.I., T.P., L.P., C.H.G., P.F., D.M., R.H., J.K.) and Institute of Medical Statistics and Bioinformatics (M.G.H.), Faculty of Medicine, University Hospital Cologne, University of Cologne, Kerpener Strasse 62, 50937 Cologne, Germany
| | - Simon Lennartz
- From the Institute of Diagnostic and Interventional Radiology (R.J.G., T.D., A.C.B., S.L., A.I.I., T.P., L.P., C.H.G., P.F., D.M., R.H., J.K.) and Institute of Medical Statistics and Bioinformatics (M.G.H.), Faculty of Medicine, University Hospital Cologne, University of Cologne, Kerpener Strasse 62, 50937 Cologne, Germany
| | - Andra-Iza Iuga
- From the Institute of Diagnostic and Interventional Radiology (R.J.G., T.D., A.C.B., S.L., A.I.I., T.P., L.P., C.H.G., P.F., D.M., R.H., J.K.) and Institute of Medical Statistics and Bioinformatics (M.G.H.), Faculty of Medicine, University Hospital Cologne, University of Cologne, Kerpener Strasse 62, 50937 Cologne, Germany
| | - Martin Gunnar Hellmich
- From the Institute of Diagnostic and Interventional Radiology (R.J.G., T.D., A.C.B., S.L., A.I.I., T.P., L.P., C.H.G., P.F., D.M., R.H., J.K.) and Institute of Medical Statistics and Bioinformatics (M.G.H.), Faculty of Medicine, University Hospital Cologne, University of Cologne, Kerpener Strasse 62, 50937 Cologne, Germany
| | - Thorsten Persigehl
- From the Institute of Diagnostic and Interventional Radiology (R.J.G., T.D., A.C.B., S.L., A.I.I., T.P., L.P., C.H.G., P.F., D.M., R.H., J.K.) and Institute of Medical Statistics and Bioinformatics (M.G.H.), Faculty of Medicine, University Hospital Cologne, University of Cologne, Kerpener Strasse 62, 50937 Cologne, Germany
| | - Lenhard Pennig
- From the Institute of Diagnostic and Interventional Radiology (R.J.G., T.D., A.C.B., S.L., A.I.I., T.P., L.P., C.H.G., P.F., D.M., R.H., J.K.) and Institute of Medical Statistics and Bioinformatics (M.G.H.), Faculty of Medicine, University Hospital Cologne, University of Cologne, Kerpener Strasse 62, 50937 Cologne, Germany
| | - Carsten Herbert Gietzen
- From the Institute of Diagnostic and Interventional Radiology (R.J.G., T.D., A.C.B., S.L., A.I.I., T.P., L.P., C.H.G., P.F., D.M., R.H., J.K.) and Institute of Medical Statistics and Bioinformatics (M.G.H.), Faculty of Medicine, University Hospital Cologne, University of Cologne, Kerpener Strasse 62, 50937 Cologne, Germany
| | - Philipp Fervers
- From the Institute of Diagnostic and Interventional Radiology (R.J.G., T.D., A.C.B., S.L., A.I.I., T.P., L.P., C.H.G., P.F., D.M., R.H., J.K.) and Institute of Medical Statistics and Bioinformatics (M.G.H.), Faculty of Medicine, University Hospital Cologne, University of Cologne, Kerpener Strasse 62, 50937 Cologne, Germany
| | - David Maintz
- From the Institute of Diagnostic and Interventional Radiology (R.J.G., T.D., A.C.B., S.L., A.I.I., T.P., L.P., C.H.G., P.F., D.M., R.H., J.K.) and Institute of Medical Statistics and Bioinformatics (M.G.H.), Faculty of Medicine, University Hospital Cologne, University of Cologne, Kerpener Strasse 62, 50937 Cologne, Germany
| | - Robert Hahnfeldt
- From the Institute of Diagnostic and Interventional Radiology (R.J.G., T.D., A.C.B., S.L., A.I.I., T.P., L.P., C.H.G., P.F., D.M., R.H., J.K.) and Institute of Medical Statistics and Bioinformatics (M.G.H.), Faculty of Medicine, University Hospital Cologne, University of Cologne, Kerpener Strasse 62, 50937 Cologne, Germany
| | - Jonathan Kottlors
- From the Institute of Diagnostic and Interventional Radiology (R.J.G., T.D., A.C.B., S.L., A.I.I., T.P., L.P., C.H.G., P.F., D.M., R.H., J.K.) and Institute of Medical Statistics and Bioinformatics (M.G.H.), Faculty of Medicine, University Hospital Cologne, University of Cologne, Kerpener Strasse 62, 50937 Cologne, Germany
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Alexander R, Waite S, Bruno MA, Krupinski EA, Berlin L, Macknik S, Martinez-Conde S. Mandating Limits on Workload, Duty, and Speed in Radiology. Radiology 2022; 304:274-282. [PMID: 35699581 PMCID: PMC9340237 DOI: 10.1148/radiol.212631] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Research has not yet quantified the effects of workload or duty hours on the accuracy of radiologists. With the exception of a brief reduction in imaging studies during the 2020 peak of the COVID-19 pandemic, the workload of radiologists in the United States has seen relentless growth in recent years. One concern is that this increased demand could lead to reduced accuracy. Behavioral studies in species ranging from insects to humans have shown that decision speed is inversely correlated to decision accuracy. A potential solution is to institute workload and duty limits to optimize radiologist performance and patient safety. The concern, however, is that any prescribed mandated limits would be arbitrary and thus no more advantageous than allowing radiologists to self-regulate. Specific studies have been proposed to determine whether limits reduce error, and if so, to provide a principled basis for such limits. This could determine the precise susceptibility of individual radiologists to medical error as a function of speed during image viewing, the maximum number of studies that could be read during a work shift, and the appropriate shift duration as a function of time of day. Before principled recommendations for restrictions are made, however, it is important to understand how radiologists function both optimally and at the margins of adequate performance. This study examines the relationship between interpretation speed and error rates in radiology, the potential influence of artificial intelligence on reading speed and error rates, and the possible outcomes of imposed limits on both caseload and duty hours. This review concludes that the scientific evidence needed to make meaningful rules is lacking and notes that regulating workloads without scientific principles can be more harmful than not regulating at all.
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Affiliation(s)
- Robert Alexander
- From the Departments of Ophthalmology (R.A., S.M., S.M.C.), Radiology (S.W.), Neurology (S.M., S.M.C.), and Physiology & Pharmacology (S.M., S.M.C.), SUNY Downstate Health Sciences University, 450 Clarkson Ave, Brooklyn, NY 11203; Department of Radiology, Penn State Milton S. Hershey Medical Center, Hershey, Pa (M.A.B.); Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (E.A.K.); and Department of Radiology, Rush University Medical College and University of Illinois, Chicago, Ill (L.B.)
| | - Stephen Waite
- From the Departments of Ophthalmology (R.A., S.M., S.M.C.), Radiology (S.W.), Neurology (S.M., S.M.C.), and Physiology & Pharmacology (S.M., S.M.C.), SUNY Downstate Health Sciences University, 450 Clarkson Ave, Brooklyn, NY 11203; Department of Radiology, Penn State Milton S. Hershey Medical Center, Hershey, Pa (M.A.B.); Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (E.A.K.); and Department of Radiology, Rush University Medical College and University of Illinois, Chicago, Ill (L.B.)
| | - Michael A Bruno
- From the Departments of Ophthalmology (R.A., S.M., S.M.C.), Radiology (S.W.), Neurology (S.M., S.M.C.), and Physiology & Pharmacology (S.M., S.M.C.), SUNY Downstate Health Sciences University, 450 Clarkson Ave, Brooklyn, NY 11203; Department of Radiology, Penn State Milton S. Hershey Medical Center, Hershey, Pa (M.A.B.); Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (E.A.K.); and Department of Radiology, Rush University Medical College and University of Illinois, Chicago, Ill (L.B.)
| | - Elizabeth A Krupinski
- From the Departments of Ophthalmology (R.A., S.M., S.M.C.), Radiology (S.W.), Neurology (S.M., S.M.C.), and Physiology & Pharmacology (S.M., S.M.C.), SUNY Downstate Health Sciences University, 450 Clarkson Ave, Brooklyn, NY 11203; Department of Radiology, Penn State Milton S. Hershey Medical Center, Hershey, Pa (M.A.B.); Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (E.A.K.); and Department of Radiology, Rush University Medical College and University of Illinois, Chicago, Ill (L.B.)
| | - Leonard Berlin
- From the Departments of Ophthalmology (R.A., S.M., S.M.C.), Radiology (S.W.), Neurology (S.M., S.M.C.), and Physiology & Pharmacology (S.M., S.M.C.), SUNY Downstate Health Sciences University, 450 Clarkson Ave, Brooklyn, NY 11203; Department of Radiology, Penn State Milton S. Hershey Medical Center, Hershey, Pa (M.A.B.); Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (E.A.K.); and Department of Radiology, Rush University Medical College and University of Illinois, Chicago, Ill (L.B.)
| | - Stephen Macknik
- From the Departments of Ophthalmology (R.A., S.M., S.M.C.), Radiology (S.W.), Neurology (S.M., S.M.C.), and Physiology & Pharmacology (S.M., S.M.C.), SUNY Downstate Health Sciences University, 450 Clarkson Ave, Brooklyn, NY 11203; Department of Radiology, Penn State Milton S. Hershey Medical Center, Hershey, Pa (M.A.B.); Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (E.A.K.); and Department of Radiology, Rush University Medical College and University of Illinois, Chicago, Ill (L.B.)
| | - Susana Martinez-Conde
- From the Departments of Ophthalmology (R.A., S.M., S.M.C.), Radiology (S.W.), Neurology (S.M., S.M.C.), and Physiology & Pharmacology (S.M., S.M.C.), SUNY Downstate Health Sciences University, 450 Clarkson Ave, Brooklyn, NY 11203; Department of Radiology, Penn State Milton S. Hershey Medical Center, Hershey, Pa (M.A.B.); Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (E.A.K.); and Department of Radiology, Rush University Medical College and University of Illinois, Chicago, Ill (L.B.)
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Spilseth B, McKnight CD, Li MD, Park CJ, Fried JG, Yi PH, Brian JM, Lehman CD, Wang XJ, Phalke V, Pakkal M, Baruah D, Khine PP, Fajardo LL. AUR-RRA Review: Logistics of Academic-Industry Partnerships in Artificial Intelligence. Acad Radiol 2022; 29:119-128. [PMID: 34561163 DOI: 10.1016/j.acra.2021.08.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 07/29/2021] [Accepted: 08/07/2021] [Indexed: 12/27/2022]
Abstract
The Radiology Research Alliance (RRA) of the Association of University Radiologists (AUR) convenes Task Forces to address current topics in radiology. In this article, the AUR-RRA Task Force on Academic-Industry Partnerships for Artificial Intelligence, considered issues of importance to academic radiology departments contemplating industry partnerships in artificial intelligence (AI) development, testing and evaluation. Our goal was to create a framework encompassing the domains of clinical, technical, regulatory, legal and financial considerations that impact the arrangement and success of such partnerships.
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Affiliation(s)
- Benjamin Spilseth
- Department of Radiology, University of Minnesota Medical School, Minneapolis, Minnesota
| | - Colin D McKnight
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Matthew D Li
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Christian J Park
- Department of Radiology, Penn State Health, Milton S. Hershey Center, Hershey, Pennsylvania
| | - Jessica G Fried
- Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - Paul H Yi
- Department of Radiology and Diagnostic Imaging, University of Maryland Intelligent Imaging (UMII) Center, University of Maryland School of Medicine & Malone Center for Engineering in Healthcare, Johns Hopkins University Whiting School of Engineering, Baltimore, Maryland
| | - James M Brian
- Department of Radiology, Penn State Health, Penn State Children's Hospital, Penn State Milton S. Hershey Medical Center, Hershey, Pennsylvania
| | - Constance D Lehman
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | | | - Vaishali Phalke
- Department of Radiology, University of Florida, Gainesville, Florida
| | - Mini Pakkal
- Department of Radiology, University of Toronto, Toronto, Canada
| | - Dhiraj Baruah
- Department of Radiology and Radiological Science; Medical University of South Carolina, Charleston, South Carolina
| | - Pwint Phyu Khine
- Penn State Health Milton S. Hershey Medical Center, Hershey, PA, USA
| | - Laurie L Fajardo
- Department of Radiology and Radiological Sciences, University of Utah, 1950 Circle of Hope - 3rd floor Breast Imaging Clinic, Salt Lake City, UT 84112.
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