eDoctor: machine learning and the future of medicine

• Published in: Journal of internal medicine Vol. 284; no. 6; pp. 603 - 619
• Main Authors: Handelman, G. S., Kok, H. K., Chandra, R. V., Razavi, A. H., Lee, M. J., Asadi, H.
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 01.12.2018
• Subjects: Artificial intelligence; Global health; Health care; Learning algorithms; Machine learning; Medical personnel; Medicine; Precision medicine; supervised machine learning; unsupervised machine learning; supervised machine learning; unsupervised machine learning; medicine; machine learning; artificial intelligence
• ISSN: 0954-6820; 1365-2796; 1365-2796
• DOI: 10.1111/joim.12822

Machine learning (ML) is a burgeoning field of medicine with huge resources being applied to fuse computer science and statistics to medical problems. Proponents of ML extol its ability to deal with large, complex and disparate data, often found within medicine and feel that ML is the future for biomedical research, personalized medicine, computer‐aided diagnosis to significantly advance global health care. However, the concepts of ML are unfamiliar to many medical professionals and there is untapped potential in the use of ML as a research tool. In this article, we provide an overview of the theory behind ML, explore the common ML algorithms used in medicine including their pitfalls and discuss the potential future of ML in medicine.