Leveraging GPT-4 enables patient comprehension of radiology reports
M. H. Elise van Driel, Noa Blok, Jan A.J.G. van den Brand, Davy van de Sande, Marianne de Vries, Bram Eijlers, Fokko Smits, Jacob J. Visser, Diederik Gommers, Cornelis Verhoef, Michel E. van Genderen, Dirk J. Grünhagen, Denise E. Hilling
Abstract
OBJECTIVE: To assess the feasibility of using GPT-4 to simplify radiology reports into B1-level Dutch for enhanced patient comprehension. METHODS: This study utilised GPT-4, optimised through prompt engineering in Microsoft Azure. The researchers iteratively refined prompts to ensure accurate and comprehensive translations of radiology reports. Two radiologists assessed the simplified outputs for accuracy, completeness, and patient suitability. A third radiologist independently validated the final versions. Twelve colorectal cancer patients were recruited from two hospitals in the Netherlands. Semi-structured interviews were conducted to evaluate patients' comprehension and satisfaction with AI-generated reports. RESULTS: The optimised GPT-4 tool produced simplified reports with high accuracy (mean score 3.33/4). Patient comprehension improved significantly from 2.00 (original reports) to 3.28 (simplified reports) and 3.50 (summaries). Correct classification of report outcomes increased from 63.9% to 83.3%. Patient satisfaction was high (mean 8.30/10), with most preferring the long simplified report. CONCLUSION: RADiANT successfully enhances patient understanding and satisfaction through automated AI-driven report simplification, offering a scalable solution for patient-centred communication in clinical practice. This tool reduces clinician workload and supports informed patient decision-making, demonstrating the potential of LLMs beyond English-based healthcare contexts.