ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case Study on Simplified Radiology Reports
Katharina Jeblick1,2 Balthasar Schachtner1 Jakob Dexl1,3 Andreas Mittermeier1 Anna Theresa Stüber1,4 Johanna Topalis1 Tobias Weber1,3,4 Philipp Wesp1 Bastian Sabel1 Jens Ricke1 Michael Ingrisch1,3 1Department of Radiology, University Hospital, LMU Munich 2Comprehensive Pneumology Center (CPC-M), Member of the German Center for Lung Research (DZL), Munich 3Munich Center for Machine Learning (MCML) 4Department of Statistics, LMU Munich {katharina.jeblick,
Abstract
The release of ChatGPT, a language model capable of generating text that appears human-like and authentic, has gained significant attention beyond the research community. We expect that the convincing performance of ChatGPT incentivizes users to apply it to a variety of downstream tasks, including prompting the model to simplify their own medical reports. To investigate this phenomenon, we conducted an exploratory case study. In a questionnaire, we asked 15 radiologists to assess the quality of radiology reports simplified by ChatGPT. Most radiologists agreed that the simplified reports were factually correct, complete, and not potentially harmful to the patient. Nevertheless, instances of incorrect statements, missed key medical findings, and potentially harmful passages were reported. While further studies are needed, the initial insights of this study indicate a great potential in using large language models like ChatGPT to improve patient-centered care in radiology and other medical domains. ††All authors contributed equally. †† ††The title was generated with ChatGPT.
原文 arXiv:2212.14882;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2212.14882v1