临床医生的医疗人工智能:失去的认知视角
Lana Tikhomirov1, Carolyn Semmler2, Melissa McCradden3
1Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia.
The Lancet. Digital health
|July 26, 2024
概括
医疗诊断中的人工智能 (AI) 发展迅速,但对其临床价值的理解却滞后. 这项研究突出了人类临床医生和人工智能决策之间的关键差异,这对于安全的放射学人工智能集成至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 放射学 放射学是一门学科.
- 认知科学 认知科学
背景情况:
- 人工智能 (AI) 在医疗决策支持系统中的快速发展和商业化,特别是在放射学中,超过了临床医生对其真正价值的理解.
- 当前的AI模型往往缺乏人类临床决策过程中固有的上下文理解和认知灵活性.
研究的目的:
- 用生态有限的推理来描述放射科医生的诊断决策.
- 将临床医生的决策与人工智能模型决策进行比较,以确定人工智能集成的挑战.
- 提出未来的研究方向,以提高临床医生-AI互动,安全性和可用性.
主要方法:
- 通过生态界限推理的透视来分析临床医生的决策.
- 人类临床医生对认知过程的比较审查与AI决策模型.
- 鉴定临床医生和人工智能之间的能力错位.
主要成果:
- 临床医生是情境激励的,有资源的决策者,而AI模型是情境剥离的,相关的决策者.
- 在将AI整合到放射学中存在重大挑战,原因是决策能力的根本差异.
- 关于临床医生与人工智能相互作用的误解源于这种错误对齐.
结论:
- 解决认知和上下文差异对于有效的临床医生-AI合作至关重要.
- 未来的研究必须侧重于决策的认知方面,以提高AI在高风险医疗环境中的安全性和可用性.
- 对这些差异的更好理解将促进AI在临床实践中的负责任实施.
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