How Machine Learning Is Crafting Precision Medicine

Medicine has become more and more individualized since the days of leeches and humors, but in the last 15 years, an explosion of patient data in the form of genetic information and electronic health records (EHRs) has sharpened the doctor’s picture of the individual patient—and of treatments tailored to their precise needs.

Such targeted care is referred to as precision medicine—drugs or treatments designed for small groups, rather than large populations, based on characteristics such as medical history, genetic makeup, and data recorded by wearable devices. In 2003, the completion of the Human Genome Project was attended by fanatic promises about the imminence of these treatments, but results have so far underwhelmed. Today, new technologies are revitalizing the promise.

Precision medicine: drugs or treatments designed for small groups, rather than large populations.

At organizations ranging from large corporations to university-led and government-funded research collectives, doctors are using artificial intelligence (AI) to develop precision treatments for complex diseases. Their central aim is to glean from increasingly massive and available data sets insight into what makes patients healthy at the individual level. Those insights could guide the development of new drugs, uncover new uses for old ones, suggest personalized combinations, and predict disease risk.

Nearly 80% of respondents to a recent Oracle Health Sciences survey says they expect AI and machine learning to improve treatment recommendations, and in a 2017 paper, Dr. Bertalan Meskó, director of the Medical Futurist Institute, suggested that “there is no precision medicine without AI.” His point, albeit forward-looking, acknowledges that without AI to analyze it, patient data will remain severely untapped.

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