Utilizing Large Language Models to Simplify Radiology Reports: a comparative analysis of ChatGPT3.5, ChatGPT4.0, Google Bard, and Microsoft Bing

Abstract

Abstract This paper investigates the application of Large Language Models (LLMs), specifically OpenAI’s ChatGPT3.5, ChatGPT4.0, Google Bard, and Microsoft Bing, in simplifying radiology reports, thus potentially enhancing patient understanding. We examined 254 anonymized radiology reports from diverse examination types and used three different prompts to guide the LLMs’ simplification processes. The resulting simplified reports were evaluated using four established readability indices. All LLMs significantly simplified the reports, but performance varied based on the prompt used and the specific model. The ChatGPT models performed best when additional context was provided (i.e., specifying user as a patient or requesting simplification at the 7th grade level). Our findings suggest that LLMs can effectively simplify radiology reports, although improvements are needed to ensure accurate clinical representation and optimal readability. These models have the potential to improve patient health literacy, patient-provider communication, and ultimately, health outcomes.

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