Rosenbacke Rikard, Melhus Åsa, McKee Martin, Stuckler David
Centre for Corporate Governance, Department of Accounting, Copenhagen Business School, Frederiksberg, Denmark
Department of Medical Sciences, Uppsala University, Uppsala, Sweden.
J Med Ethics. 2025 May 21;51(6):396-399. doi: 10.1136/jme-2024-110074.
Can AI substitute a human physician's second opinion? Recently the published two contrasting views: Kempt and Nagel advocate for using artificial intelligence (AI) for a second opinion except when its conclusions significantly diverge from the initial physician's while Jongsma and Sand argue for a second human opinion irrespective of AI's concurrence or dissent. The crux of this debate hinges on the prevalence and impact of 'false confirmation'-a scenario where AI erroneously validates an incorrect human decision. These errors seem exceedingly difficult to detect, reminiscent of heuristics akin to confirmation bias. However, this debate has yet to engage with the emergence of explainable AI (XAI), which elaborates on why the AI tool reaches its diagnosis. To progress this debate, we outline a framework for conceptualising decision-making errors in physician-AI collaborations. We then review emerging evidence on the magnitude of false confirmation errors. Our simulations show that they are likely to be pervasive in clinical practice, decreasing diagnostic accuracy to between 5% and 30%. We conclude with a pragmatic approach to employing AI as a second opinion, emphasising the need for physicians to make clinical decisions before consulting AI; employing nudges to increase awareness of false confirmations and critically engaging with XAI explanations. This approach underscores the necessity for a cautious, evidence-based methodology when integrating AI into clinical decision-making.
人工智能能替代人类医生的二次诊断意见吗？最近有两篇发表的观点相互对立的文章：肯普特和纳格尔主张在人工智能得出的结论与最初医生的结论没有显著差异时，使用人工智能提供二次诊断意见；而容斯马和桑德则主张无论人工智能的意见是赞同还是反对，都应寻求第二位人类医生的意见。这场辩论的关键在于“错误确认”的普遍性和影响，即人工智能错误地验证了错误的人类决策。这些错误似乎极难被发现，这让人联想到类似于确认偏差的启发式思维。然而，这场辩论尚未涉及可解释人工智能（XAI）的出现，可解释人工智能能够详细说明人工智能工具是如何得出诊断结果的。为了推动这场辩论，我们概述了一个用于概念化医生与人工智能合作中决策错误的框架。然后，我们回顾了关于错误确认错误程度的新证据。我们的模拟结果表明，这些错误在临床实践中可能很普遍，会将诊断准确率降低到5%至30%之间。我们最后提出了一种将人工智能用作二次诊断意见的务实方法，强调医生在咨询人工智能之前需要做出临床决策；利用助推手段提高对错误确认的认识，并认真对待可解释人工智能的解释。这种方法强调了在将人工智能整合到临床决策中时，采用谨慎、基于证据的方法的必要性。
