THREE PROBLEMS WITH BIG DATA AND ARTIFICIAL INTELLIGENCE IN MEDICINE
THREE PROBLEMS WITH BIG DATA AND ARTIFICIAL INTELLIGENCE IN MEDICINE
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DOI：
10.1353/pbm.2019.0012
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发表时间：
2019-03-01
影响因子：
1
通讯作者：
Upshur, Ross
中科院分区：
文献类型：
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作者：
Chin-Yee, Benjamin;Upshur, Ross
大数据和人工智能（AI）在医疗保健领域的兴起引起了相当大的兴奋，声称可以改善诊断、预后和治疗方法。在这种热情中，医学中大数据和人工智能运动背后的哲学假设很少被研究。本文概述了这一运动所面临的三个哲学挑战：（1）由大数据和测量的理论负载引起的认识论-本体论问题;（2）由算法的固有局限性以及随之而来的可靠性和可解释性问题引起的认识论-逻辑问题;（3）关于人类经验不可还原为定量数据的现象学问题。这些哲学问题表明了这些技术在整合到临床护理之前必须考虑的几个重要挑战。我们的文章旨在就大数据和人工智能在医疗保健中的影响展开批判性对话，以便对这些技术进行更有力的评估，并帮助开发更好地为临床医生及其患者服务的临床护理方法。
The rise of big data and artificial intelligence (AI) in health care has engendered considerable excitement, claiming to improve approaches to diagnosis, prognosis, and treatment. Amidst the enthusiasm, the philosophical assumptions that underlie the big data and AI movement in medicine are rarely examined. This essay outlines three philosophical challenges faced by this movement: (1) the epistemological-ontological problem arising from the theory-ladenness of big data and measurement; (2) the epistemological-logical problem resulting from the inherent limitations of algorithms and attendant issues of reliability and interpretability; and (3) the phenomenological problem concerning the irreducibility of human experience to quantitative data. These philosophical issues demonstrate several important challenges for these technologies that must be considered prior to their integration into clinical care. Our article aims to initiate a critical dialogue on the impact of big data and AI in health care in order to allow for more robust evaluation of these technologies and to aid in the development of approaches to clinical care that better serve clinicians and their patients.