Translating Radiology Reports into Plain Language using ChatGPT and GPT-4 with Prompt Learning: Promising Results, Limitations, and Potential
Qing Lyu, Josh Tan, Michael E. Zapadka, Janardhana Ponnatapura, Chuang Niu, Kyle J. Myers, Ge Wang, Christopher T. Whitlow
TL;DR
This study investigates translating radiology reports into plain language for patients and clinicians using ChatGPT, with a GPT-4 comparison to assess potential educational value. It employs a prompt-based workflow to generate translations and patient/provider suggestions for 62 chest CT and 76 brain MRI reports, evaluated by radiologists on completeness, correctness, and overall quality, revealing substantial improvements with optimized prompts and GPT-4. Key findings show notable reductions in report length, high overall translation quality (average score around 4.27/5), and strong but imperfect coverage of findings, with randomness and occasional omissions arising from prompts. The results suggest AI-assisted translation can support clinical education and communication, but emphasize the need for careful prompt design, assessment of safety and completeness, and regulatory pathways before clinical deployment.
Abstract
The large language model called ChatGPT has drawn extensively attention because of its human-like expression and reasoning abilities. In this study, we investigate the feasibility of using ChatGPT in experiments on using ChatGPT to translate radiology reports into plain language for patients and healthcare providers so that they are educated for improved healthcare. 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 the 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 general relevant such as keeping following-up with doctors and closely monitoring any symptoms, and for about 37% of 138 cases in total 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 a newly released large model 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.