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INTRODUCTION

Machine learning has reshaped our consumer lives, with self-driving vehicles, conversant digital assistants, and machine translation services so ubiquitous that they are at risk of not being considered particularly intelligent for much longer. Will the algorithms underlying these technologies similarly transform the art and practice of medicine? There is hope that modern machine-learning techniques—especially the resurgence of artificial neural networks in deep learning—will foster a sea change in clinical practice that augments both the sensory and diagnostic powers of physicians while, perhaps paradoxically, freeing physicians to spend more time with their patients by performing laborious tasks.

From the birth of artificial intelligence at the Dartmouth Summer Research Project on Artificial Intelligence in 1956 to self-driving vehicles today, machine-learning methods and theory have developed in symbiosis with growing datasets and computational power. In this chapter, we discuss the foundations of modern machine-learning algorithms and their emerging applications in clinical practice. Modern machine-learning techniques are sufficiently capacious as to learn flexible and rich representations of clinical data and are remarkably adept at exploiting spatial and temporal structure in raw data. The newest machine-learning models perform on par with expert physicians or prior state-of-the art models on a variety of tasks, such as the interpretation of images (e.g., grading retinal fundus photographs for diabetic retinopathy), analysis of unstructured text (e.g., predicting hospital readmission from electronic health record notes), and processing of speech (e.g., detecting depression from patient speech). However, many evaluations of machine-learning models occur on tasks that are narrow and unrealistic, and further lack the clinical context that a physician would incorporate. The models themselves are also often divorced from considerations of patient utility. To help ensure these models benefit patients, this chapter aims to bring more physicians into the design and evaluation of machine-learning models by providing an understanding of how modern machine-learning models are developed and how they relate to more familiar methods from the epidemiological literature.

Today, the terms machine learning and artificial intelligence evoke images distinct from those conjured up by the same terms in the 1950s and the 1980s, and they likely will mean something different in a decade. Computer scientist John McCarthy originally defined artificial intelligence in 1956 as “the broad science and engineering of creating intelligent machines,” most often embodied today as computer software. Machine learning can be viewed as the subfield of artificial intelligence encompassing algorithms that extract generalizable patterns from data. This stands in contrast to approaches to create intelligence from human-engineered and explicitly programmed rules that characterized many early applications of artificial intelligence to medicine during the 1970s and 1980s (e.g., expert systems such as INTERNIST-I and MYCIN).

This chapter covers machine-learning methods and applications that may augment physician expertise at the point of care. Many applications of machine learning in health care are therefore not reviewed here, for example algorithms to improve hospital planning, detect insurance fraud, and monitor new drugs for adverse ...

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