利用GPT-4能使患者理解放射学报告。

Leveraging GPT-4 enables patient comprehension of radiology reports.

作者信息

van Driel M H Elise, Blok Noa, van den Brand Jan A J G, van de Sande Davy, de Vries Marianne, Eijlers Bram, Smits Fokko, Visser Jacob J, Gommers Diederik, Verhoef Cornelis, van Genderen Michel E, Grünhagen Dirk J, Hilling Denise E

机构信息

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.

出版信息

Eur J Radiol. 2025 Jun;187:112111. doi: 10.1016/j.ejrad.2025.112111. Epub 2025 Apr 11.

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.

摘要

目的

评估使用GPT-4将放射学报告简化为B1级荷兰语以提高患者理解度的可行性。

方法

本研究使用了在微软Azure中通过提示工程优化的GPT-4。研究人员迭代完善提示,以确保放射学报告的准确和全面翻译。两名放射科医生评估简化后的输出在准确性、完整性和患者适用性方面的情况。第三名放射科医生独立验证最终版本。从荷兰的两家医院招募了12名结直肠癌患者。进行了半结构化访谈,以评估患者对人工智能生成报告的理解和满意度。

结果

优化后的GPT-4工具生成的简化报告准确性高(平均得分3.33/4)。患者的理解度从2.00(原始报告)显著提高到3.28(简化报告)和3.50(摘要)。报告结果的正确分类从63.9%提高到83.3%。患者满意度很高(平均8.30/10),大多数患者更喜欢较长的简化报告。

结论

RADiANT通过自动化的人工智能驱动的报告简化成功提高了患者的理解度和满意度,为临床实践中以患者为中心的沟通提供了一种可扩展的解决方案。该工具减轻了临床医生的工作量,并支持患者做出明智的决策,证明了大语言模型在非英语医疗环境中的潜力。

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