诊断决策中的人工智能和计算机辅助诊断:5个医学信息学和人机界面研究问题
Tad T Brunyé1, Stephen R Mitroff2, Joann G Elmore3
1Center for Applied Brain and Cognitive Sciences, Tufts University, Medford, MA 02155, United States.
Journal of the American Medical Informatics Association : JAMIA
|October 16, 2025
概括
人工智能 (AI) 可以改善医疗诊断,但需要仔细的人类-AI合作. 研究必须解决人工智能信息的呈现方式,它对临床医生的技能的影响以及安全整合的自动化偏见.
科学领域:
- 医疗信息学 医疗信息学
- 人与计算机的交互 (HCI)
- 医疗保健中的人工智能 (AI)
背景情况:
- 诊断错误在医学中很普遍,影响患者的治疗结果.
- 人工智能有可能提高诊断准确性和效率.
- 整合人工智能决策支持系统面临的人类-人工智能协作和信任方面的挑战.
研究的目的:
- 确定人工智能在医疗信息学中的有效整合的关键研究问题.
- 探索影响AI在临床决策中的使用的人为因素.
- 为人工智能支持的医疗保健提供人机界面 (HCI) 的未来研究提供指导.
主要方法:
- 专注于基于AI的临床决策支持系统,包括计算机视觉和自然语言处理.
- 审查了关于诊断错误和AI有效性和风险的证据.
- 综合了医学,认知科学和HCI的见解,以制定研究问题.
主要成果:
- 人工智能可以减少感知和解释错误,但它的影响取决于信息呈现.
- 风险包括吸引注意力,过度依赖,自动化偏见和潜在的技能衰退.
- 可解释的人工智能提供了透明度,但也可能引入偏见.
结论:
- 以人为中心和适应性AI设计对于有效的集成至关重要.
- 五个核心研究问题涉及AI信息传递,可解释性,偏见和技能维护.
- 人工智能应该补充,而不是取代人类专业知识,以确保安全有效的医疗保健.
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