Leveraging GPT-4 enables patient comprehension of radiology reports.

Authors

van Driel MHE,Blok N,van den Brand JAJG,van de Sande D,de Vries M,Eijlers B,Smits F,Visser JJ,Gommers D,Verhoef C,van Genderen ME,Grünhagen DJ,Hilling DE

Affiliations (4)

  • Department of Surgical Oncology and Gastrointestinal Surgery, Erasmus MC Cancer Centre, University Medical Centre Rotterdam, the Netherlands.
  • Erasmus MC Datahub, Erasmus University Medical Centre, Rotterdam, the Netherlands; Department of Adult Intensive Care, Erasmus University Medical Centre, Rotterdam, the Netherlands.
  • Department of Radiology & Nuclear Medicine, Erasmus University Medical Centre, Rotterdam, the Netherlands.
  • Department of Surgical Oncology and Gastrointestinal Surgery, Erasmus MC Cancer Centre, University Medical Centre Rotterdam, the Netherlands; Erasmus MC Datahub, Erasmus University Medical Centre, Rotterdam, the Netherlands. Electronic address: [email protected].

Abstract

To assess the feasibility of using GPT-4 to simplify radiology reports into B1-level Dutch for enhanced patient comprehension. 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. 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. 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.

Topics

ComprehensionColorectal NeoplasmsRadiology Information SystemsJournal Article
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