Provision of Radiology Reports Simplified With Large Language Models to Patients With Cancer: Impact on Patient Satisfaction.

IF 2.8 Q2 ONCOLOGY JCO Clinical Cancer Informatics Pub Date : 2025-01-01 Epub Date: 2025-01-29 DOI:10.1200/CCI-24-00166
Amit Gupta, Swarndeep Singh, Hema Malhotra, Himanshu Pruthi, Aparna Sharma, Amit K Garg, Mukesh Yadav, Devasenathipathy Kandasamy, Atul Batra, Krithika Rangarajan
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Abstract

Purpose: To explore the perceived utility and effect of simplified radiology reports on oncology patients' knowledge and feasibility of large language models (LLMs) to generate such reports.

Materials and methods: This study was approved by the Institute Ethics Committee. In phase I, five state-of-the-art LLMs (Generative Pre-Trained Transformer-4o [GPT-4o], Google Gemini, Claude Opus, Llama-3.1-8B, and Phi-3.5-mini) were tested to simplify 50 oncology computed tomography (CT) report impressions using five distinct prompts with each LLM. The outputs were evaluated quantitatively using readability indices. Five LLM-prompt combinations with best average readability scores were also assessed qualitatively, and the best LLM-prompt combination was selected. In phase II, 100 consecutive oncology patients were randomly assigned into two groups: original report (received original report impression) and simplified report (received LLM-generated simplified versions of their CT report impressions under the supervision of a radiologist). A questionnaire assessed the impact of these reports on patients' knowledge and perceived utility.

Results: In phase I, Claude Opus-Prompt 3 (explain to a 15-year-old) performed slightly better than other LLMs, although scores for GPT-4o, Gemini, Claude Opus, and Llama-3.1 were not significantly different (P > .0033 on Wilcoxon signed-rank test with Bonferroni correction). In phase II, simplified report group patients demonstrated significantly better knowledge of primary site and extent of their disease as well as showed significantly higher confidence and understanding of the report (P < .05 for all). Only three (of 50) simplified reports required corrections by the radiologist.

Conclusion: Simplified radiology reports significantly enhanced patients' understanding and confidence in comprehending their medical condition. LLMs performed very well at this simplification task; therefore, they can be potentially used for this purpose, although there remains a need for human oversight.

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向癌症患者提供用大语言模型简化的放射学报告:对患者满意度的影响
目的:探讨简化的放射学报告对肿瘤患者知识的感知效用和影响,以及大型语言模型(llm)生成此类报告的可行性。材料和方法:本研究经研究所伦理委员会批准。在第一阶段,测试了五个最先进的LLM(生成预训练变形器40 [gpt - 40],谷歌Gemini, Claude Opus, lama-3.1- 8b和pi -3.5-mini),以简化50个肿瘤计算机断层扫描(CT)报告印象,每个LLM使用五个不同的提示。使用可读性指标对产出进行定量评价。对5个平均可读性得分最高的llm提示组合进行定性评估,选出最佳的llm提示组合。在第二阶段,连续100例肿瘤患者被随机分为两组:原始报告(接受原始报告印象)和简化报告(在放射科医生的监督下接受llm生成的简化版CT报告印象)。一份调查问卷评估了这些报告对患者知识和感知效用的影响。结果:在第一阶段,Claude Opus- prompt 3(向15岁的人解释)的表现略好于其他LLMs,尽管gpt - 40、Gemini、Claude Opus和Llama-3.1的得分没有显著差异(经Bonferroni校正的Wilcoxon sign -rank检验P为0.0033)。在II期,简化报告组患者对原发部位和疾病程度的了解明显更好,对报告的置信度和理解程度也明显更高(P < 0.05)。在50份简化报告中,只有3份需要放射科医生进行更正。结论:简化的影像学报告能显著提高患者对自身病情的认识和信心。llm在这个简化任务中表现得非常好;因此,它们可以潜在地用于这一目的,尽管仍然需要人为监督。
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4.80%
发文量
190
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