Abstract
While radiologists can describe a fracture’s morphology and complexity with ease, the translation into classification systems such as the Arbeitsgemeinschaft Osteosynthesefragen (AO) Fracture and Dislocation Classification Compendium is more challenging. We tested the performance of generic chatbots and chatbots aware of specific knowledge of the AO classification provided by a vector-index and compared it to human readers. In the 100 radiological reports we created based on random AO codes, chatbots provided AO codes significantly faster than humans (mean 3.2 s per case vs. 50 s per case, p
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CITATION STYLE
Russe, M. F., Fink, A., Ngo, H., Tran, H., Bamberg, F., Reisert, M., & Rau, A. (2023). Performance of ChatGPT, human radiologists, and context-aware ChatGPT in identifying AO codes from radiology reports. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-41512-8
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