Artificial Intelligence and the Implementation Challenge
Artificial Intelligence and the Implementation Challenge
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DOI：
10.2196/13659
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发表时间：
2019-07-10
影响因子：
7.4
通讯作者：
Goldfarb, Avi
中科院分区：
文献类型：
--
作者：
Shaw, James;Rudzicz, Frank;Goldfarb, Avi
背景资料：人工智能（AI）在医疗保健领域的应用近年来备受关注，但AI带来的实施问题尚未得到实质性的解决。Objective：在本文中，我们重点讨论了机器学习（ML）作为AI的一种形式，并提供了一个框架来思考ML在医疗保健领域的用例。我们已经组织了我们的讨论的挑战，在实施ML与其他技术相比，使用的框架不采用，放弃，和挑战的规模，传播和可持续性的健康和护理技术（NASSS）。方法：在提供了AI技术的概述后，我们描述ML的用例属于决策支持和自动化的类别。我们建议这些用例适用于临床，操作和流行病学任务，ML在医疗保健中的主要功能在短期内将是决策支持。然后，我们概述了NASSS框架所解决的类别中ML计划所带来的独特实施问题，特别是包括有意义的决策支持，可解释性，隐私，同意，算法偏见，安全性，可扩展性，公司的角色以及医疗保健工作不断变化的性质。最终，我们认为ML在医疗保健领域的未来仍然是积极的，但不确定的，因为患者，公众，和广泛的卫生保健利益相关者是必要的，使其有意义的实施。结论：如果实施科学界要以能够产生广泛利益的方式促进ML的采用，本文件提出的问题在今后几年中将需要给予大量关注。
Background: Applications of artificial intelligence (AI) in health care have garnered much attention in recent years, but the implementation issues posed by AI have not been substantially addressed.Objective: In this paper, we have focused on machine learning (ML) as a form of AI and have provided a framework for thinking about use cases of ML in health care. We have structured our discussion of challenges in the implementation of ML in comparison with other technologies using the framework of Nonadoption, Abandonment, and Challenges to the Scale-Up, Spread, and Sustainability of Health and Care Technologies (NASSS).Methods: After providing an overview of AI technology, we describe use cases of ML as falling into the categories of decision support and automation. We suggest these use cases apply to clinical, operational, and epidemiological tasks and that the primary function of ML in health care in the near term will be decision support. We then outline unique implementation issues posed by ML initiatives in the categories addressed by the NASSS framework, specifically including meaningful decision support, explainability, privacy, consent, algorithmic bias, security, scalability, the role of corporations, and the changing nature of health care work.Results: Ultimately, we suggest that the future of ML in health care remains positive but uncertain, as support from patients, the public, and a wide range of health care stakeholders is necessary to enable its meaningful implementation.Conclusions: If the implementation science community is to facilitate the adoption of ML in ways that stand to generate widespread benefits, the issues raised in this paper will require substantial attention in the coming years.