[论文解读] The promise and perils of AI in medicine

Robert Sparrow, Joshua Hatherley|Monash University Research Portal (Monash University)|May 11, 2025

Artificial Intelligence in Healthcare and Education被引用 9

一句话总结

对医学领域人工智能的希望与风险进行综述与批判性评估，涵盖研究、诊断、管理、关怀的人性化、隐私、偏见与治理。

ABSTRACT

What does Artificial Intelligence (AI) have to contribute to health care? And what should we be looking out for if we are worried about its risks? In this paper we offer a survey, and initial evaluation, of hopes and fears about the applications of artificial intelligence in medicine. AI clearly has enormous potential as a research tool, in genomics and public health especially, as well as a diagnostic aid. It's also highly likely to impact on the organisational and business practices of healthcare systems in ways that are perhaps under-appreciated. Enthusiasts for AI have held out the prospect that it will free physicians up to spend more time attending to what really matters to them and their patients. We will argue that this claim depends upon implausible assumptions about the institutional and economic imperatives operating in contemporary healthcare settings. We will also highlight important concerns about privacy, surveillance, and bias in big data, as well as the risks of over trust in machines, the challenges of transparency, the deskilling of healthcare practitioners, the way AI reframes healthcare, and the implications of AI for the distribution of power in healthcare institutions. We will suggest that two questions, in particular, are deserving of further attention from philosophers and bioethicists. What does care look like when one is dealing with data as much as people? And, what weight should we give to the advice of machines in our own deliberations about medical decisions?

研究动机与目标

• 评估人工智能在医学研究、诊断和管理方面的潜在收益。
• 识别与医疗保健中人工智能相关的伦理、政治和社会风险。
• 考察人工智能可能如何影响患者护理、医生角色和医疗机构。
• 讨论哲学家和生物伦理学家应就以数据为中心的护理和医疗决策中的机器引导提出的问题。

提出的方法

• 对医学领域中AI在研究、诊断和管理领域的现有文献进行批判性调查。
• 分析与医疗保健中的AI系统相关的伦理、隐私、偏见和可解释性挑战。
• 讨论对信任、责任以及医疗机构内部权力分配的影响。
• 主张在医学中谨慎地、以哲学为基础的整合AI的方法。

实验结果

研究问题

• RQ1 人工智能在医学研究、诊断和管理方面的主要承诺是什么？
• RQ2 在AI驱动的医学中，由隐私、监控、偏见和可解释性带来的风险和伦理关切是什么？
• RQ3 人工智能如何影响信任、医生技能下降、系统脆弱性和医疗保健中的权力分配？
• RQ4 哲学家和生物伦理学家应优先考虑哪些关于数据为中心的护理和机器影响的医疗决策的问题？

主要发现

• 人工智能有潜力通过基因组学、药物发现和电子病历数据挖掘推进医学研究。
• AI在诊断支持方面显示出潜力，尤其是在医学影像学，但临床验证有限且方法学常常薄弱。
• AI可能改善医疗管理和资源分配，但可能使控制权集中并影响患者体验。
• 由于大规模数据收集和潜在的再识别风险，隐私和监控问题因AI而加剧。
• 训练数据中的偏见可能使健康不平等在种族、性别和阶层方面被延续或恶化。
• 可解释性挑战引发了对患者自主权、知情同意和可辩解的医疗决策的问题。