How Machine Learning Will Transform Biomedicine

Abstract

This Perspective explores the application of machine learning toward improved diagnosis and treatment. We outline a vision for how machine learning can transform three broad areas of biomedicine: clinical diagnostics, precision treatments, and health monitoring, where the goal is to maintain health through a range of diseases and the normal aging process. For each area, early instances of successful machine learning applications are discussed, as well as opportunities and challenges for machine learning. When these challenges are met, machine learning promises a future of rigorous, outcomes-based medicine with detection, diagnosis, and treatment strategies that are continuously adapted to individual and environmental differences.

Figure 1.

How machine learning applications could help individuals maintain health. At home, machine learning may help in early detection of disease, monitoring response to treatment, and adherence to treatment regimens. In the clinic or hospital, machine learning may aid medical professionals to diagnosis and tune treatment for an individual patient. The dashed line shows how a patient moves between home and clinical settings and how machine learning can help at each step to maintain health.

Figure 2.

Combining data collected from both home (left) and clinical settings (right), or combining predictive models built at home and in the clinic, has the potential to lead to comprehensive and integrated models that support personalized health management. Comprehensive models are more likely to perform well as they incorporate more information about an individual, and these models have the potential to be applied in the home, clinic, or wherever an individual may be.