Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia.
School of Psychology, University of Adelaide, Adelaide, SA, Australia.
Lancet Digit Health. 2024 Aug;6(8):e589-e594. doi: 10.1016/S2589-7500(24)00095-5.
The development and commercialisation of medical decision systems based on artificial intelligence (AI) far outpaces our understanding of their value for clinicians. Although applicable across many forms of medicine, we focus on characterising the diagnostic decisions of radiologists through the concept of ecologically bounded reasoning, review the differences between clinician decision making and medical AI model decision making, and reveal how these differences pose fundamental challenges for integrating AI into radiology. We argue that clinicians are contextually motivated, mentally resourceful decision makers, whereas AI models are contextually stripped, correlational decision makers, and discuss misconceptions about clinician-AI interaction stemming from this misalignment of capabilities. We outline how future research on clinician-AI interaction could better address the cognitive considerations of decision making and be used to enhance the safety and usability of AI models in high-risk medical decision-making contexts.
基于人工智能的医学决策系统的开发和商业化远远超出了我们对其对临床医生价值的理解。尽管适用于许多形式的医学，但我们专注于通过生态边界推理的概念来描述放射科医生的诊断决策，回顾临床医生决策和医学人工智能模型决策之间的差异，并揭示这些差异如何对将人工智能整合到放射学中构成根本性挑战。我们认为，临床医生是具有情境动机、思维敏捷的决策者，而人工智能模型则是情境剥夺、相关决策的决策者，并讨论了由于能力不匹配而导致的关于临床医生-人工智能交互的误解。我们概述了未来关于临床医生-人工智能交互的研究如何更好地解决决策的认知考虑因素，并用于提高高风险医疗决策环境中人工智能模型的安全性和可用性。
