The Stakes of Uncertainty: Developing and Integrating Machine Learning in Clinical Care

Abstract

The wide-spread deployment of machine learning tools within healthcare is on the horizon. However, the hype around “AI” tends to divert attention toward the spectacular, and away from the more mundane and ground- level aspects of new technologies that shape technological adoption and integration. This paper examines the development of a machine learning-driven sepsis risk detection tool in a hospital Emergency Department in order to interrogate the contingent and deeply contextual ways in which AI technologies are likely be adopted in healthcare. In particular, the paper bring into focus the epistemological implications of introducing a machine learning-driven tool into a clinical setting by analyzing shifting categories of trust, evidence, and authority. The paper further explores the conditions of certainty in the disciplinary contexts of data science and ethnography, and offers a potential reframing of the work of doing data science and machine learning as “computational ethnography” in order to surface potential pathways for developing effective, human-centered AI.

Keywords: AI, machine learning, deep learning, healthcare, sepsis, medicine, anthropology