eDoctor: machine learning and the future of medicine

By G. Handelman, H. Kok, R. Chandra, Amir Hossein Razavi, M. Lee, H. Asadi. Journal of Internal Medicine, 2018

787 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Health Information Management

View Paper: https://www.semanticscholar.org/paper/70f6937b6253db8209d8fd6a4115e766946f04c5

Citations in the corpus

• Artificial Intelligence in Healthcare: Review and Prediction Case Studies (2020, neutral): “In addition to being able to act as an ‘‘eDoctor” for disease diagnosis, management, and prognosis, AI has unexplored usage as a powerful tool in biomedical research [57].”