Machine Learning in Medicine:

Abstract

A 49-year-old patient notices a painless rash on his shoulder but does not seek care. Months later, his wife asks him to see a doctor, who diagnoses a seborrheic keratosis. Later, when the patient undergoes a screening colonoscopy, a nurse notices a dark macule on his shoulder and advises him to have it evaluated. One month later, the patient sees a dermatologist, who obtains a biopsy specimen of the lesion. The findings reveal a noncancerous pigmented lesion. Still concerned, the dermatologist requests a second reading of the biopsy specimen, and invasive melanoma is diagnosed. An oncologist initiates treatment with systemic chemotherapy. A physician friend asks the patient why he is not receiving immunotherapy. W hat if every medical decision, whether made by an intensivist or a community health worker, was instantly reviewed by a team of relevant experts who provided guidance if the decision seemed amiss? Patients with newly diagnosed, uncomplicated hypertension would receive the medications that are known to be most effective rather than the one that is most familiar to the prescriber. 1,2 Inadvertent overdoses and errors in prescribing would be largely eliminated. 3,4 Patients with mysterious and rare ailments could be directed to renowned experts in fields related to the suspected diagnosis. 5 Such a system seems far-fetched. There are not enough medical experts to staff it, it would take too long for experts to read through a patient's history, and concerns related to privacy laws would stop efforts before they started. 6 Yet, this is the promise of machine learning in medicine: the wisdom contained in the decisions made by nearly all clinicians and the outcomes of billions of patients should inform the care of each patient. That is, every diagnosis, management decision, and therapy should be personalized on the basis of all known information about a patient, in real time, incorporating lessons from a collective experience. This framing emphasizes that machine learning is not just a new tool, such as a new drug or medical device. Rather, it is the fundamental technology required to meaningfully process data that exceed the capacity of the human brain to comprehend ; increasingly, this overwhelming store of information pertains to both vast clinical databases and even the data generated regarding a single patient. 7 Nearly 50 years ago, a Special Article in the Journal stated that computing would be "augmenting and, in some cases, largely replacing the intellectual functions of the physician." 8 Yet, in early 2019, surprisingly little in health care is driven by machine learning. Rather than report the myriad proof-of-concept models (of retrospective data) that have been tested, here we describe the core structural changes and paradigm shifts in the health care system that are necessary to enable the full promise of machine learning in medicine (see video). M achine Le a r ning E x pl a ined Traditionally, software engineers have distilled knowledge in the form of explicit computer code that instructs computers exactly how to process data and how to A video overview of machine learning is available at NEJM.org