解决数字健康的生成人工智能的6个挑战:一个范围审查
Tara Templin1,2, Monika W Perez3, Sean Sylvia2,1,4
1Department of Health Policy and Management, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
PLOS digital health
|May 23, 2024
概括
生成型人工智能 (AI) 在数字健康中存在诸如偏见和隐私问题等风险. 本综述强调了使用人工智能工具的医疗从业人员面临的关键挑战和缓解策略.
科学领域:
- 数字健康数字健康
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
背景情况:
- 生成型人工智能 (AI) 在数字健康应用中提供了巨大的潜力.
- 然而,生成性AI工具具有固有的局限性,包括偏见,数据隐私问题以及产生不准确信息 (幻觉) 的可能性.
- 了解这些限制对于在医疗环境中安全有效实施至关重要.
研究的目的:
- 在医疗环境中对与生成人工智能相关的挑战进行范围审查.
- 识别和综合这些挑战的潜在解决方案和缓解策略.
- 为数字卫生从业者提供有关使用生成性AI的风险和最佳实践的信息.
主要方法:
- 使用PubMed.com进行了全面的文献搜索.
- 确定并分析了2024年3月之前发表的120篇评估医学生成AI的文章.
- 从选定的文献中综合了主题和建议.
主要成果:
- 确定并讨论了数字健康从业人员面临的六个关键挑战.
- 常见的问题包括偏见,数据隐私,幻觉和监管合规性.
- 较少评估的挑战包括过度依赖文本模型,对抗性错误提示和越狱.
结论:
- 在医学中采用生成人工智能需要仔细考虑其局限性.
- 需要积极的战略来减轻诸如偏见和隐私侵犯等风险.
- 需要进行进一步的研究,以应对医疗保健的生成人工智能的未经探索的挑战.
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