人工智能醫學應用的前景與風險: The Promise and Perils of AI in Medicine - SPARROW, Robert; HATHERLEY, Joshua (International Journal of Chinese & Comparative Philosophy of Medicine, 2019)
LANGUAGE NOTE | Document text in English; abstract also in Chinese. 人工智能（ＡＩ）將如何促進人類的醫療保健？如果我們擔心人工智能介入醫療的風險，我們又應該關注什麽呢？本文試圖概述此類問題，並對人工智能介入醫療的風險與希望作一個初步評價。人工智能作為一種研究工具和診斷工具具有巨大的潛力，特別是在基因組學和公共衛生領域中。人工智能在醫療中的廣泛使用可能還會對醫療系統的組織方式和商業實踐產生深刻的影響，而這些影響的方式與程度還沒有被充分認識到。在人工智能醫學的熱情擁護者看來，應用人工智能可以幫助醫生集中精力在對他們和病人而言真正重要的問題上。然而，本文將論證這些樂觀的判斷是基於對現代醫療環境下機構和經濟運行規則的一些不合情理的假設之上。本文將聚焦於如下一 些重要議題：大資料中的隱私、監管和偏見，過分信任機器的風險，透明度問題，醫療專業人士的“去技能化”問題，人工智能重塑醫療保健的方式，以及人工智能對醫療保健中權力分配的影響。其中有兩個關鍵的問題尤其值得哲學家和生命倫理學家的進一步關注。第一，當醫生不僅需要處理人而且需要處理資料的時候，醫療實踐會呈現出什麽樣的形態？第二，在醫療決策權衡中，我們應該给予來自機器的意見以多大的權重？ 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? DOWNLOAD HISTORY | This article has been downloaded 119 times in Digital Commons before migrating into this platform.