Artificial intelligence: what’s possible, why now?

Computers can identify images in a constrained environment, but they do a poor job at understanding a scene in an unconstrained environment, he said. “If you had to locate someone by looking at the back of their head in a picture taken from behind a crowd of people, you would still be able to do this much of the time,” Dr. Singh said. “The accuracy of a computer in a task like this would be below 10 percent.”

This limited capacity for pattern recognition in the absence of full context limits AI’s role in medicine. Radiologists and pathologists did not just learn to analyze images—like other humans, “they learned to recognize patterns through 50 million years of evolution, four years of residency, years of fellowship, and maybe some life experience,” Dr. Singh said.

Mimicking the impact that evolution has had on the perceptive abilities of humans would require that the computer analyze 7 billion patients’ worth of data to diagnose breast cancer alone with as much accuracy as a domain expert. “Providing that amount of data is not feasible,” Dr. Singh said. A connectionist-only approach with no a priori knowledge from humans will never work, especially for a problem that has an inherent high dimensionality.

Allowing the computer to learn from scratch by observing has its pitfalls as well. “If a computer were placed at a traffic light in New York City and learned the rules of traffic lights by observing human behavior, it would learn that red means stop, green means keep going, and yellow means speed up,” Dr. Singh said. “It’s no different with diagnosing breast cancer. The system will only learn the average of all humans. But who wants to be an average physician or be diagnosed or treated by an average doctor?”

Selectively inputting data from only the best physicians is an option, but that body of data would be too small, Dr. Singh said.

“It’s a catch-22.”

Despite these limitations, there will be successful applications of AI in medicine, including in time IBM Watson, for which the initial expectations were unrealistic, he says. In Silicon Valley alone, there are now about 40 startups that address pathology exclusively, in areas ranging from molecular profile analysis to NGS-based liquid biopsies to image analysis for breast, brain, and prostate cancer. If a startup dies, he says, someone else will pick up the asset and make something of it.

In medicine, there is plenty of AI activity to be learned from. The AI successes are modeling and capturing existing knowledge and making it available. Examples are arrhythmia recognition from electrocardiograms, coronary heart disease risk-group detection, monitoring prescription of restricted-use antibiotics, and early melanoma diagnosis. The latter is the use of AI to classify skin lesions (Esteva A, et al. Nature. 2017;​542:​115–118).

The applications that are modeling and learning net new knowledge to improve human performance are promising, he said, among them Cellworks for oncology therapy selection, Genxsys decision support for GPs, and Zebra Medical for radiologists. “If you want to build net new knowledge, that can be done,” Dr. Singh said, “but you have to constrain the environment. You can’t create a generalist out of it, but you can create a specialist or super-specialist.”

Breast cancer is an unconstrained problem, he said. “Too much complexity, too many dimensions, the number of biomarkers, the type of images, family history, and so on.” Pneumonia and melanoma are more constrained, for example. “If the data you have available is large and the dimensionality is low,” you have an AI solution, he said.

In the near term, Dr. Singh predicted the AI industry will focus on 10 broad categories of health care applications. (See “Top 10 AI applications in health care.”)

Three virtual nursing assistant applications—Sense.ly, Tavie, and Ada—are working in the United States, England, and Canada, Dr. Singh said. Chatbots are and will remain another popular application of AI, with apps like Babylon Health, which mixes AI and live physician interaction through video and text. The United Kingdom’s National Health Service adopted the app as an alternative to the NHS’ 111-telephone helpline, which patients call for health care advice and to be directed to local or after-hours medical services. “Now, when you call in for a basic triage at the NHS, you’ll interact with a chatbot first,” Dr. Singh said.

Predicting the course of AI in the long term is more difficult, but one thing it will likely do is produce “a patient of the future that is different from the patient of today,” Dr. Singh said. “The patient of the future will be wired with devices that provide a lot of data and allow us to pick up trend lines.”
The evolution to a wired human has begun, in fact, with instruments that patients can attach to their mobile devices to examine their mouth, throat, eyes, heart, lungs, skin, and body temperature (TytoCare) to help clinicians diagnose a variety of conditions remotely, and mobile apps to aggregate data and monitor patients’ emotional health and alert caregivers when symptoms are problematic (Ginger.io).

Care delivery will also change as AI improves and proliferates.

“Device cameras will have very high resolution, systems will use natural language processing, and we will have robotics in our ecosystem.”

Among the recent innovations that have changed how care is provided are technologies that use retinal self-imaging to help clinicians detect illnesses ranging from glaucoma to multiple sclerosis (eyeSelfie, Rimokon) and an app for monitoring wound healing postoperatively (Parable).

Advanced robotics, more sophisticated learning systems, analytics, and communication speed will all improve health system operations, Dr. Singh said. Current applications include a system that helps prevent readmissions and save money by using predictive analytics to optimize where frontline staff are deployed (Care at Hand), autonomous robots that transport materials and clinical supplies throughout a facility (Aethon Tug Robot), and “smartglasses” that make it possible for clinicians to see high-definition images of the vasculature as they insert an intravenous device.

For precision medicine, the tools available or in development include the world’s largest database of human genotypes (Human Longevity), a cell culture platform that mimics the architecture and physiology of the human liver (LiverChip), and nanorobots built from designer DNA that deliver drugs to specific cell types (Wyss Institute).

Pathologists and laboratories can take immediate steps to integrate AI into their operations and care and get ahead of the AI curve, Dr. Singh said.

“Start digitizing and indexing your data now,” he recommended. “Even if you don’t use digital pathology, even if you never want to read on screen, start digitizing because those data will be critical someday.”

He also suggested storing correlated clinical data. Electronic health records store longitudinal data. “As newer, practical implementations of AI become available over the next decade, we will be able to do a lot more with the longitudinal data than we currently do. Hence, we must start storing correlated data—images, clinical data, and all—longitudinally. So run an experiment and say, ‘When can you get the correlated clinical data for the current images?’”

Another is to start a pilot project to verify the accuracy of coding. “Lab administrators often complain about coding being a problem, leading to claim rejections,” he said. “AI can already help with that now. There are systems that work rather well.”

Another pilot to implement: using chatbots for genetic counseling. “There are not enough genetic counselors,” he said, “and as much as 80 percent of their work can be done by a chatbot.”

David Wild is a writer in Toronto.