Three Problems with Big Data and Artificial Intelligence in Medicine
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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.
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