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Artificial Intelligence Transforms the Future of Health Care

doctor in uniform using futuristic looking digital screens and keyboard

Life sciences researchers using artificial intelligence (AI) are under pressure to innovate faster than ever. Large, multilevel, and integrated data sets offer the promise of unlocking novel insights and accelerating breakthroughs. Although more data are available than ever, only a fraction is being curated, integrated, understood, and analyzed. AI focuses on how computers learn from data and mimic human thought processes. AI increases learning capacity and provides decision support system at scales that are transforming the future of health care. This article is a review of applications for machine learning in health care with a focus on clinical, translational, and public health applications with an overview of the important role of privacy, data sharing, and genetic information.

Machine learning, a popular subdiscipline of artificial intelligence (AI), uses large data sets and identifies interaction patterns among variables. These techniques can discover previously unknown associations, generate novel hypotheses, and drive researchers and resources toward most fruitful directions.1 Machine learning can be applied in various fields, such as financial, automatic driving, smart home, etc. In medicine, machine learning is widely used to build automated clinical decision systems.

Most approaches to machine learning fall into two main categories: supervised and unsupervised. Supervised methods are great for classification and regression. Recent examples include detection of a lung nodule from a chest X-ray2; risk estimation models of anticoagulation therapy;3 implantation of automated defibrillators in cardiomyopathy;4 use in classification of stroke and stroke mimic;5 modeling of CD4+ T cell heterogeneity;6outcome prediction in infectious diseases;7 detection of arrhythmia in electrocardiogram (ECG);8 and design and development of in silico clinical trial9 among others.

Unsupervised learning does not require labeled data. It aims to identify hidden patterns present in the data and is often used in data exploration and in the generation of novel hypotheses.2 In three separate studies in heart failure with preserved ejection fraction among patients who had a heterogeneous condition with no proven therapies without human intervention,10 researchers used unsupervised learning2 to revisit failed clinical trials such as treatment with spironolactone,11 enalapril,12 and sildenafil13 compared with placebo to identify a subclass of patients who might benefit from specific therapies.

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-Nariman Noorbakhsh-Sabet, MDa,b, Ramin Zand, MD, MPHc,b,d, Yanfei Zhang, PhDe, Vida Abedi, PhDf,d,

This article originally appeared in the July 2019 issue of The American Journal of Medicine.

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