An Introduction to Machine Learning for Clinicians

Abstract

The technology at the heart of the most innovative progress in health care artificial intelligence (AI) is in a subdomain called machine learning (ML), which describes the use of software algorithms to identify patterns in very large datasets. ML has driven much of the progress of health care AI over the past 5 years, demonstrating impressive results in clinical decision support, patient monitoring and coaching, surgical assistance, patient care, and systems management. Clinicians in the near future will find themselves working with information networks on a scale well beyond the capacity of human beings to grasp, thereby necessitating the use of intelligent machines to analyze and interpret the complex interactions between data, patients, and clinical decision makers. However, as this technology becomes more powerful, it also becomes less transparent, and algorithmic decisions are therefore progressively more opaque. This is problematic because computers will increasingly be asked for answers to clinical questions that have no single right answer and that are open-ended, subjective, and value laden. As ML continues to make important contributions in a variety of clinical domains, clinicians will need to have a deeper understanding of the design, implementation, and evaluation of ML to ensure that current health care is not overly influenced by the agenda of technology entrepreneurs and venture capitalists. The aim of this article is to provide a nontechnical introduction to the concept of ML in the context of health care, the challenges that arise, and the resulting implications for clinicians.