GPT-4生成准确和可读的患者教育材料,与当前的瘤指南保持一致:随机评估
Severin Rodler1,2,3, Francesco Cei1,2,4, Conner Ganjavi1,2
1USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, University of Southern California, Los Angeles, California, United States of America.
PloS one
|June 4, 2025
概括
生成型人工智能 (GenAI) 可以创建比传统人工智能更易读,更准确的患者教育材料 (PEMs). 这项技术还可以将PEM翻译成多种语言,改善患者对泌尿癌的理解.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 患者教育材料 (PEMs) 对于患者赋权至关重要,但需要经常更新和适应可读性.
- 现有的PEM通常在可读性方面存在挑战,并且可能并不总是最新的.
- 生成型人工智能 (GenAI) 为创建可访问和当前的教育内容提供了一个潜在的解决方案.
研究的目的:
- 评估GenAI (特别是GPT-4) 在产生可读,准确和最新的尿道癌PEM的能力.
- 与现有材料相比,评估GenAI生成的PEM的质量和可读性.
- 为了确定将GenAI生成的PEM翻译成多种语言的有效性.
主要方法:
- 根据欧洲泌尿病学协会 (EAU) 针对前列腺,膀,脏和丸癌的指南,GPT-4被用来产生PEM.
- 32位泌尿科医生评估了原始与GenAI生成的PEM的准确性,完整性和清晰性.
- 翻译成五种欧盟语言的翻译,由母语人士对其真实性和清晰性进行了评价.
主要成果:
- 基因人工智能在平均52.1秒内快速产生PEM.
- 通过GenAI生成的PEM显示出明显更好的可读性得分 (例如,比原始PEM更高的Flesch Kincaid阅读方便性).
- 由GenAI生成的PEM的准确性,完整性和清晰度与原始材料相比,翻译显示出良好的忠实性和清晰度.
结论:
- 基因人工智能,特别是GPT-4,可以在保持准确性和完整性的同时,产生具有卓越可读性的患者教育材料.
- 将GenAI与人类监督相结合,可以简化创建最新,准确和可访问的PEM.
- 这种方法有可能显著减少工作量,并提高患者对不同语言群体的泌尿癌信息的理解.
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