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BACKGROUND Effective doctor-patient communication is essential in clinical practice, especially in oncology, where radiology reports play a crucial role. These reports are often filled with technical jargon, making them challenging for patients to understand and affecting their engagement and decision-making. Large Language Models (LLMs), such as Generative Pretrained Transformer-4 (GPT-4), offer a novel approach to simplifying these reports and potentially enhancing communication and patient outcomes. OBJECTIVE To assess the feasibility and effectiveness of using GPT-4 to simplify oncological radiology reports to improve doctor-patient communication. METHODS In a retrospective study approved by the Ethics Review Committees of multiple hospitals, 698 radiology reports of malignant tumors from October to December 2023 were analyzed. Seventy reports were selected to develop templates and scoring scales for GPT-4 to create simplified interpretative radiology reports (IRRs). Radiologists checked the consistency between original radiology reports (ORRs) and IRRs, while middle-aged volunteers with high school education and no medical background assessed readability. Doctors evaluated communication efficiency through simulated consultations. RESULTS Transforming ORRs into IRRs resulted in clearer reports, with word count increasing from 818.74 to 1025.82 (P CONCLUSIONS This study demonstrates the significant potential of LLMs, specifically GPT-4, to facilitate medical communication by simplifying oncological radiology reports. Simplified reports enhance patient understanding and the efficiency of doctor-patient interactions, suggesting a valuable application of AI in clinical practice to improve patient outcomes and healthcare communication. CLINICALTRIAL No application.
Yang et al. (Sat,) studied this question.
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