Precision Medicine Leveraging AI, Machine Learning in Early Stage


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.

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.

Due to its rapid growth, genetic data has been at the center of discussions about individualizing treatments, but it’s only one course in the feast that will satisfy AI’s nutritional requirements. “The genome is not enough,” says Eric Topol, a geneticist, cardiologist, and director of the Scripps Research Translational Institute. “Much more can be gleaned, much more strides can be made once we start working with this multi-modal data.”

By applying AI and machine learning to these multiple data sources—genetic data, EHRs, sensor data, environmental and lifestyle data—researchers are taking first steps toward developing personalized treatments for diseases from cancer to depression. Here’s a look at the biggest opportunities—and challenges.

AI-Based Precision Medicine In Early Stage

A variety of organizations are just starting to explore AI-based approaches to precision medicine. Deep Genomics, a Toronto startup, uses AI to reduce the amount of costly trial and error in drug discovery by analyzing large genomic databases, but its first clinical trial won’t be held until 2020. All of Us, a National Institutes of Health (NIH) research program, aims to collect data on 1 million patients to advance the study of precision medicine. It began enrolling participants in May 2018, and its goal is to create a massive database of patient information that research organizations, through various methods including AI, can analyze to develop precision treatments.

In 2016, one major diagnostic platform using AI was reported to have diagnosed a woman’s cancer by analyzing her genetic data. But the software was later revealed to have grossly over-promised on capabilities. It also recommended unsafe treatment options.

In one of Topol’s research projects, he uses deep learning techniques to study the genetic, cardiovascular, and microbiomic data of “special populations,” such as 90-year-olds, to discover patterns that make them healthy. Researchers might use these patterns to develop drugs that disable harmful genes; doctors might use them to predict who’s at risk of disease. “It’s not a clinical project at this juncture,” says Topol. “There are a lot of things sitting in the data, like treasures, that haven’t been discovered yet, because they haven’t had deep learning applied.”

Read the source article in Forbes.