Bennett Holman (Underwood International College, Yonsei University) - Last year may be remembered as the pivotal point for artificial “deep learning” and medicine. A large number of different labs have used Artificial intelligence (AI) to augment some portion of medical practice, most notably in diagnosis and prognosis. I will first review the recent accomplishments of deep-learning AI in the medical field, including: the landmark work of Esteva et al. (2017) which showed that AI could learn to diagnose skin cancer better than a dermatologist; extensions of similar projects into detecting breast cancer (Liu et al., 2017); Oakden-Rayner et al.’s (2017) work showing AI could create its own ontological categories for patient risk; and through analyzing tumor DNA identify more possible sites for intervention (Wrzeszczynski et al., 2017). I will next argue that a forseeable progression of this technology is to begin automating treatment decisions. Whether this development is positive or negative depends on the specific details of who develops this technology and how it is used. I will not attempt to predict the future, but I will run out some emerging trends to their logical conclusions and identify some possible pitfalls of the gradual elimination of human judgment from medical practice. In particular some problems could become significantly worse. It is the essence of deep learning AI that reasons for its outcomes are opaque. Many researchers have shown that industry has been adept at causing confusion by advancing alternative narratives (e.g. Oreskes and Conway, 2010), but at the very least with traditional research there were assumptions that could, in principle, be assessed. With this deep learning AI there are no such luxuries. On the other hand, I will argue that properly implemented deep learning solves a number of pernicious problems with both the technical and the social hindrances to reliable medical judgments (e.g. the end to a necessary reliance on industry data). Given the multiple possible routes that such technology can take, I argue that consideration of how medical AI should develop is an issue that will not wait and thus demands immediate critical attention of philosophy of science in practice.
