Patient-centric radiology: Utilising large language models (LLMs) to improve patient communication and education

Abstract

Abstract Purpose To evaluate whether large language models (LLMs) can enhance clinician-patient communication by simplifying radiology reports to improve patient readability and comprehension. Methods A randomised controlled trial was conducted at a single healthcare service for patients undergoing X-ray, ultrasound or computed tomography between May 2025 and June 2025. Participants were randomised in a 1:1 ratio to receive either (1) the formal radiology report only or (2) the formal radiology report and an LLM-simplified version. Readability scores, including the Simple Measure of Gobbledygook, Automated Readability Index, Flesch Reading Ease, and Flesch-Kincaid grade level, were calculated for both reports. Statistical analysis of patient readability and comprehension levels, factual accuracy and hallucination rates for LLMs was assessed using a combination of binary and 5-point Likert scales, open-ended survey questions, and independent review by two radiologists. Results 59/120 patients were randomised to receive both the formal and LLM-simplified radiology reports. Readability of LLM-simplified reports significantly improved with the reading level required for formal reports equivalent to a university-standard (11 th -13 th grade) compared to a middle-school standard (5 th -9 th grade) for simplified reports (rank biserial correlation=0.83, p<0.001). Patients with both reports demonstrated a significantly greater comprehension level, with 95% reporting an understanding level greater than 50%, compared with 46% without the simplified report (rank biserial correlation = 0.67, p < 0.001). All LLM-simplified reports were considered at least somewhat accurate with a minimal hallucination rate of 1.7%. Importantly, no hallucinations resulted in potential patient harm. 118/120 (98.3%) patients expressed interest in simplified radiology reports to be included in future clinical practice. Conclusion This study provides evidence that LLMs can simplify radiology reports to an accessible level of readability with minimal hallucination. LLMs improve both ease of readability and comprehension of radiology reports for patients. Therefore, the rapid advancement of LLMs shows strong potential in enhancing patient-radiologist communication as patient access to electronic health records is increasingly adopted. Highlights Radiology reports can be complex and difficult for patients to read and interpret Strong patient demand exists for simplified radiology reports Large language models (LLMs) such as GPT-4o show promise in simplifying radiology reports LLMs credibly simplify radiology reports with minimal hallucination rates LLMs improve both patient readability and comprehension of radiology reports

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