为患者,护理人员和公众提供简单语言医疗信息的生成AI/LLM:机遇,风险和道德
Avishek Pal1, Tenzin Wangmo1, Trishna Bharadia2,3
1Institute for Biomedical Ethics, University of Basel, Basel, Switzerland.
Patient preference and adherence
|August 7, 2025
概括
生成型人工智能 (gAI) 和大型语言模型 (LLM) 提供可访问的健康信息,但存在风险. 确保准确性,透明度和道德使用对于患者安全和健康公平至关重要.
科学领域:
- 医疗保健中的人工智能
- 医疗信息学 医疗信息学
- 卫生沟通健康沟通
背景情况:
- 产生的人工智能 (gAI) 和大语言模型 (LLMs) 越来越多地被公众用于医疗信息.
- 这些人工智能工具为患者的自我护理,健康素养和临床医生的参与提供了潜在的好处.
- 然而,人工智能提供的不准确或不可靠的信息可能会导致严重的负面健康结果.
研究的目的:
- 审查已发表的关于使用gAI/LLM用于简单语言医疗信息的机会和风险的研究结果.
- 确定与准确性,可读性和潜在危害相关的关键主题.
- 探索在医疗保健中对负责任的人工智能实施的伦理考虑和建议.
主要方法:
- 对2023年1月至2024年7月间发表的44篇文章进行系统审查.
- 分析的重点是机会,风险,准确性,可读性和患者参与.
- 综合了关于共同风险和道德问题的调查结果.
主要成果:
- 研究重点关注可读性和准确性,AI的响应通常是准确的,可读的和详细的.
- 常见的风险包括过度简化,过度概括,复杂查询的精度降低,以及缺乏来源透明度.
- 很少有研究涉及到实际的患者,突出了现实世界用户体验数据的差距.
结论:
- 人工智能工具对医疗信息有希望,但需要提高透明度,治理和监控.
- 解决诸如健康公平,包容性和数据隐私等伦理问题至关重要.
- 对于非专业用户来说,对AI工具最佳利用的教育对于安全有效的采用至关重要.
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