Centre of Genomics and Policy, McGill University, Faculty of Medicine and Health Sciences, Montreal, Quebec, Canada.
Centre of Genomics and Policy, McGill University, Faculty of Medicine and Health Sciences, Montreal, Quebec, Canada.
Can J Cardiol. 2022 Feb;38(2):225-233. doi: 10.1016/j.cjca.2021.10.009. Epub 2021 Nov 1.
Nowhere is the influence of artificial intelligence (AI) likely to be more profoundly felt than in health care, from patient triage and diagnosis to surgery and follow-up. Over the medium-term, these effects will be more acute in the cardiovascular imaging context, in which AI models are already successfully performing at approximately human levels of accuracy and efficiency in certain applications. Yet, the adoption of unexplainable AI systems for cardiovascular imaging still raises significant legal and ethical challenges. We focus in particular on challenges posed by the unexplainable character of deep learning and other forms of sophisticated AI modelling used for cardiovascular imaging by briefly outlining the systems being developed in this space, describing how they work, and considering how they might generate outputs that are not reviewable by physicians or system programmers. We suggest that an unexplainable tendency presents 2 specific ethico-legal concerns: (1) difficulty for health regulators; and (2) confusion about the assignment of liability for error or fault in the use of AI systems. We suggest that addressing these concerns is critical for ensuring AI's successful implementation in cardiovascular imaging.
人工智能（AI）的影响可能在医疗保健领域体现得最为明显，从患者分诊和诊断到手术和随访。从中期来看，这些影响在心血管成像领域将更为突出，因为 AI 模型在某些应用中已经成功地达到了接近人类水平的准确性和效率。然而，对于心血管成像来说，采用不可解释的 AI 系统仍然带来了重大的法律和伦理挑战。我们特别关注深度学习和其他用于心血管成像的复杂 AI 建模形式的不可解释性所带来的挑战，简要概述了该领域正在开发的系统，描述了它们的工作原理，并考虑了它们如何生成无法由医生或系统程序员审查的输出。我们认为，不可解释的倾向带来了 2 个具体的伦理法律问题：（1）健康监管机构的困难；（2）在使用 AI 系统时，对错误或故障责任的归属感到困惑。我们认为，解决这些问题对于确保 AI 在心血管成像中的成功实施至关重要。
