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An Introduction to Machine Learning for Clinicians

Rowe, Michael PhD

doi: 10.1097/ACM.0000000000002792
Perspective: PDF Only

The technology at the heart of the most innovative progress in health care artificial intelligence (AI) is in a sub-domain called machine learning (ML), which describes the use of software algorithms to identify patterns in very large data sets. ML has driven much of the progress of health care AI over the past five 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, 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.

M. Rowe is associate professor and departmental chairperson, Department of Physiotherapy, Faculty of Community and Health Sciences, University of the Western Cape, Cape Town, South Africa; ORCID:

Funding/Support: None reported.

Other disclosures: None reported.

Ethical approval: Reported as not applicable.

Correspondence should be addressed to Michael Rowe, Department of Physiotherapy, Faculty of Community and Health Sciences, University of the Western Cape, Bellville, 7535, Cape Town, South Africa; email:; Twitter: @michael_rowe.

© 2019 by the Association of American Medical Colleges