TRANSLATING RADIOLOGY REPORTS INTO PLAIN LANGUAGE USING CHATGPT AND GPT-4 WITH PROMPT LEARNING: PROMISING RESULTS, LIMITATIONS, AND POTENTIAL

The large language model called ChatGPT has drawn extensive attention because of its human-like expression and reasoning abilities. In this study, we investigate the feasibility of using ChatGPT to translate radiology reports into plain language for patients and healthcare providers to improve education and healthcare outcomes. Radiology reports from 62 low-dose chest CT lung cancer screening scans and 76 brain MRI metastases screening scans were collected in the first half of February for this study. According to evaluation by radiologists, ChatGPT can successfully translate radiology reports into plain language with an average score of 4.27 in the five-point system with 0.08 places of information missing and 0.07 places of misinformation. In terms of the suggestions provided by ChatGPT, they are generally relevant such as following up with doctors and monitoring symptoms, and for about 37% of 138 cases ChatGPT offers specific suggestions based on findings in the report. ChatGPT also presents some randomness in its responses with occasionally over-simplified or neglected information, which can be mitigated using a more detailed prompt. Furthermore, ChatGPT results are compared with GPT-4, showing that GPT-4 can significantly improve the quality of translated reports. Our results show that it is feasible to utilize large language models in clinical education, and further efforts are needed to address limitations and maximize their potential.

Qing Lyu, Josh Tan, Michael E Zapadka, Janardhana Ponnatapura, Christopher T Whitlow, Chuang Niu, Ge Wang, Kyle J Myers