Ability of ChatGPT to generate competent radiology reports for distal radius fracture by use of RSNA template items and integrated AO classifier

Abstract

The amount of acquired radiology imaging studies grows worldwide at a rapid pace. Novel information technology tools for radiologists promise an increase of reporting quality and as well quantity at the same time. Automated text report drafting is one branch of this development. We defined for the present study in total 9 cases of distal radius fracture. Command files structured according to a template of the Radiological Society of North America (RSNA) and to Arbeitsgemeinschaft Osteosynthese (AO) classifiers were given as input to the natural language processing tool ChatGPT. ChatGPT was tasked with drafting an appropriate radiology report. A parameter study (n = 5 iterations) was performed. An overall high appraisal of ChatGPT radiology report quality was obtained in a score card based assessment. ChatGPT demonstrates the capability to adjust output files in response to minor changes in input command files. Existing shortcomings were found in technical terminology and medical interpretation of findings. Text drafting tools might well support work of radiologists in the future. They would allow a radiologist to focus time on the observation of image details and patient pathology. ChatGPT can be considered a substantial step forward towards that aim.

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