大型语言模型在改善前列腺癌识字方面的新兴作用
Marius Geantă1,2, Daniel Bădescu1,3, Narcis Chirca1,3
1Department of Urology, "Carol Davila" University of Medicine and Pharmacy, 8 Eroii Sanitari Blvd., 050474 Bucharest, Romania.
Bioengineering (Basel, Switzerland)
|July 27, 2024
概括
像ChatGPT 3.5这样的大型语言模型 (LLM) 显示出提供精确前列腺癌信息的巨大潜力,优于其他AI模型和患者指南. 建议对医疗保健中的AI进行进一步研究.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 瘤学患者教育 瘤学患者教育
背景情况:
- 患者教育对于前列腺癌护理的知情决策至关重要.
- 像患者指南这样的传统资源在动态信息传递方面存在局限性.
- 大型语言模型 (LLM) 提供了增强健康信息传播的潜力.
研究的目的:
- 评估LLM驱动的聊天机器人 (ChatGPT 3.5,CoPilot,Gemini) 在提供前列腺癌信息方面的有效性.
- 将AI聊天机器人的性能与官方患者指南进行比较.
- 评估不同来源提供的信息的准确性,及时性,完整性和可理解性.
主要方法:
- 使用25个专家验证的前列腺癌问题进行比较分析.
- 评估聊天机器人和患者指南的响应,使用利克特级别的准确性,及时性,完整性和可理解性.
- 统计分析以量化每个LLM和患者指南的表现.
主要成果:
- 聊天GPT 3.5在所有评估指标上表现出卓越的表现,使其成为可靠的信息来源.
- 同样,CoPilot的表现也很好,但比ChatGPT 3.5.5的表现要差一点.
- 虽然患者指南具有优势,但LLM为医疗保健教育提供了先进的功能.
结论:
- 基于LLM的聊天机器人,特别是ChatGPT 3.5,代表了前列腺癌患者教育工具的重大进步.
- 人工智能在医疗保健中的整合需要持续的创新和关注伦理方面的考虑,例如潜在的偏见.
- 未来的研究应该探索人工智能偏见及其对患者结果的影响,在像欧盟人工智能法案这样不断变化的医疗保健法规的背景下.
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