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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. | URI: | https://ahro.austin.org.au/austinjspui/handle/1/19406 | DOI: | 10.1111/joim.12822 | ORCID: | 0000-0003-4275-783X 0000-0003-2475-9727 |
Journal: | Journal of internal medicine | PubMed URL: | 30102808 | Type: | Journal Article | Subjects: | artificial intelligence machine learning medicine supervised machine learning unsupervised machine learning |
Appears in Collections: | Journal articles |
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