Roman Johannes Gertz, Thomas Dratsch, Alexander Christian Bunck, Simon Lennartz, Andra-Iza Iuga, Martin Gunnar Hellmich, Thorsten Persigehl, Lenhard Pennig, Carsten Herbert Gietzen, Philipp Fervers, David Maintz, Robert Hahnfeldt, Jonathan Kottlors
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...
April 2024: Radiology