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Title: eDoctor: machine learning and the future of medicine.
Austin Authors: Handelman, G S;Kok, H K;Chandra, R V;Razavi, A H;Lee, M J;Asadi, Hamed 
Affiliation: School of Medicine, Faculty of Health, Deakin University, Waurn Ponds, Victoria, Australia
BCE Corporate Security, Ottawa, ON, Canada
Department of Radiology, Beaumont Hospital and Royal College of Surgeons in Ireland, Dublin, Ireland
Interventional Neuroradiology Service, Department of Radiology, Austin Health, Heidelberg, Victoria, Australia
Royal Victoria Hospital, Belfast, UK
Interventional Radiology Service, Northern Hospital Radiology, Epping, Victoria, Australia
Interventional Neuroradiology Service, Monash Imaging, Monash Health, Clayton, Victoria, Australia
Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
School of Information Technology and Engineering, University of Ottawa, Ottawa, ON, Canada
Issue Date: 13-Aug-2018
Date: 2018-08-13
Publication information: Journal of internal medicine 2018; 284(6): 603-619
Abstract: 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.
DOI: 10.1111/joim.12822
ORCID: 0000-0003-4275-783X
Journal: Journal of internal medicine
PubMed URL: 30102808
Type: Journal Article
Subjects: artificial intelligence
machine learning
supervised machine learning
unsupervised machine learning
Appears in Collections:Journal articles

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