Machine Learning in Medicine
2015/11/16 by Rahul C. Deo · 3,482 citations
Computer Science · Health Professions · Medicine · #Artificial Intelligence in Healthcare #ECG Monitoring and Analysis #Intensive care medicine #Machine Learning in Healthcare #Medical education #Medical physics #Medicine
paper · open access · doi:10.1161/circulationaha.115.001593
published in Circulation 132(20), 1920-1930 (Lippincott Williams & Wilkins)
openalex publication_date 2015/11/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Abstract
Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games - tasks that would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in health care. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades, and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus, part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome.
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