超越过度自信:为道德医学AI嵌入好奇心和谦卑
Sebastián Andrés Cajas Ordóñez1, Rowell Castro2, Leo Anthony Celi3,4,5
1MIT Critical Data, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
PLOS digital health
|January 6, 2026
概括
医疗AI需要表达不确定性. 博迪框架使用好奇心和谦卑来创造更安全,协作性的医疗保健人工智能,承认局限性,并在需要时移交给人类专家.
科学领域:
- 人工智能在医学中的应用
- 医疗保健 人工智能 伦理学
- 人工智能中的认知科学
背景情况:
- 当前的医学AI缺乏表达不确定性的机制,导致过度依赖和潜在的临床错误.
- 过度自信的人工智能预测在高风险的医疗环境中存在风险.
- 现有的人工智能系统往往无法识别其局限性或适应现实世界的临床复杂性.
研究的目的:
- 介绍医疗保健AI的BODHI (桥梁,开放,有见识,谦卑,询问) 框架.
- 建立好奇心和谦卑作为医学AI的核心设计原则.
- 开发可以表达不确定性的AI系统,并与临床医生有效合作.
主要方法:
- 提出基于认识美德的双反射AI架构.
- 整合积极探索的好奇心和谦卑的克制和不确定性量化.
- 设计协同反循环,以平衡探索和谨慎.
- 将心理学理论和跨物种证据纳入生物设计原则.
主要成果:
- BODHI框架使人工智能能够探索不确定性,寻找信息,并检测分布外的数据.
- 人工智能中的谦卑允许进行校准的信心,边界识别和对人类专业知识的尊重.
- 协同功能可以防止过度探索和过度谨慎,支持协作决策.
- 实施特征包括校准的信心,OOD检测,好奇心驱动的升级和自适应透明度.
结论:
- 博迪框架提供了一个从过度自信的自动化转向协作的人类-人工智能伙伴关系的范式转变.
- 嵌入不确定性意识和协作克制可以提高AI的安全性和临床实用性.
- 这种方法促进了对医疗AI的相互问责和校准的信任.
- 未来的人工智能系统应该体现停顿,反思和推迟的智慧,优先考虑安全和协作.
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