Artificial Intelligence and Black-Box Medical Decisions: Accuracy versus Explainability
Artificial Intelligence and Black-Box Medical Decisions: Accuracy versus Explainability
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DOI：
10.1002/hast.973
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发表时间：
2019-01-01
影响因子：
3.3
通讯作者：
London, Alex John
中科院分区：
文献类型：
--
作者：
London, Alex John
尽管决策算法对医学来说并不新鲜，但海量医疗数据的可获得性、计算能力的增强以及机器学习的突破正在加快它们的发展步伐，扩大它们可以解决的问题的范围，并增强它们的预测能力。然而，在许多情况下，最强大的机器学习技术购买诊断或预测的准确性是以牺牲我们获取“机器内部知识”的能力为代价的。在没有解释个别病例中特定决定的原因或理由的情况下，一些评论人士认为，将医疗决策拱手让给黑匣子系统，违反了临床医生的深刻道德责任。然而，我认为，不透明的决定在医学界比批评者意识到的更常见。此外，正如亚里士多德在两千多年前指出的那样，当我们对因果系统的知识不完整和不稳定时--就像医学中经常发生的那样--解释如何产生结果的能力可能不如产生这样的结果并从经验上验证其准确性的能力那么重要。
Although decision‐making algorithms are not new to medicine, the availability of vast stores of medical data, gains in computing power, and breakthroughs in machine learning are accelerating the pace of their development, expanding the range of questions they can address, and increasing their predictive power. In many cases, however, the most powerful machine learning techniques purchase diagnostic or predictive accuracy at the expense of our ability to access “the knowledge within the machine.” Without an explanation in terms of reasons or a rationale for particular decisions in individual cases, some commentators regard ceding medical decision‐making to black box systems as contravening the profound moral responsibilities of clinicians. I argue, however, that opaque decisions are more common in medicine than critics realize. Moreover, as Aristotle noted over two millennia ago, when our knowledge of causal systems is incomplete and precarious—as it often is in medicine—the ability to explain how results are produced can be less important than the ability to produce such results and empirically verify their accuracy.