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Potential of GPT-4 for Detecting Errors in Radiology
Reports: Implications for Reporting Accuracy
Autores: Roman Johannes Gertz, MD • Thomas Dratsch, MD • Alexander Christian Bunck, MD • Simon Lennartz, MD •
Andra-Iza Iuga, MD • Martin Gunnar Hellmich, PhD • Thorsten Persigehl, MD • Lenhard Pennig, MD •
Carsten Herbert Gietzen, MD • Philipp Fervers, MD • David Maintz, MD • Robert Hahnfeldt, MD •
Jonathan Kottlors, MD
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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 costefficient
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. |
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