Artificially intelligent tools could spot health conditions more expertly than humans, yet that won't be enough for them to go into everyday use

Can we have an expert diagnosis? Anna Schroll / fotogloria / LUZ

THE doctors were stumped. After months of cancer treatment at the University of Tokyo Hospital, the patient – a woman in her 60s – was not getting much better.

So the medical team plugged the woman’s symptoms into IBM’s Watson, the supercomputer that once famously trounced human champs in the TV quiz show Jeopardy! Watson rifled through its storehouse of oncology data and announced that she had a rare form of secondary leukemia. The team changed the treatment, and she was soon out of hospital.

Watson spotted in minutes what could otherwise have taken weeks to diagnose, one doctor told The Japan Times. “It might be an exaggeration to say AI saved her life, but it surely gave us the data we needed in an extremely speedy fashion.”


Is this the future of medicine? Artificial intelligence researchers have long dreamed of creating machines that can diagnose health conditions, suggest treatment plans to doctors, and even predict how a patient’s health will change.

The main advantage of such an AI wouldn’t be speed, but precision. A study published earlier this year found that medical error is the third leading cause of death in the US, and a significant chunk of that is incorrect diagnoses.

“There are so many health conditions and the literature changes so fast that no doctor can keep up“

There are just too many health conditions and the literature is changing too rapidly for a primary care physician to retain it all, says Herbert Chase, who works on biomedical informatics at Columbia University in New York City. “We’ve exceeded where it’s humanly possible for doctors to know what they need to know,” he says. “There are dozens of conditions that are being missed that could easily be diagnosed by a machine.”

Chase once advised the IBM Watson team. These days, he is working on an algorithm that scours doctors’ notes for subtle clues that patients may be developing multiple sclerosis. The goal is to build a program that can calculate each person’s risk of MS, whether it be 0.5 or 5 per cent. He imagines a future in which software will automatically analyse electronic health records and spit out warnings or recommendations.

“It’s a partnership. The machine makes a recommendation, then the human gets involved,” says Chase. But the spectrum of human illness is complex, so “algorithms will have to be built brick by brick”, with the focus on one medical question at a time.

These building blocks often rely on machine learning, a branch of artificial intelligence that seeks patterns in mounds of statistics. Thanks to the ease of collecting and sharing data, researchers are coming up with new algorithms as fast as computers can crunch through the numbers.

Smart slide-reader

For example, a team at Stanford University in California recently unveiled a machine-learning algorithm trained to scrutinise slides of cancerous lung tissue. The computer learned to pick out specific features about each slide, like the cells’ size, shape and texture. It could also distinguish between samples from people who had only lived for a short time after diagnosis – say, a few months – and ones from those who survived much longer. The study verified the algorithm’s results by testing it on historical data, so now the AI could in principle be used with patients.

Stanford’s slide-reader is just one in a long string of AIs that are learning to perform medical tasks. At a conference last week on machine learning and healthcare in Los Angeles, researchers presented new algorithms to detect seizures, predict the progression of kidney or heart disease, and pick out anomalies in pregnant women or newborn babies. Participants in one programming challenge are getting AIs to listen to recordings of heartbeats, sorting the normal rhythms from the abnormal.

Yet other projects are trying to make medical judgements using more obscure or indirect sources. A Microsoft algorithm, published in June, makes guesses about who has pancreatic cancer based on their web searches. Google DeepMind, based in London, is using masses of anonymised data from the UK’s National Health Service to train an AI that will help ophthalmologists. The aim here is to spot looming eye disease earlier than a human can, although the project does raise questions about whether commercial firms are gaining access to health data too cheaply (see “Getting our money’s worth“).

But is the medical profession ready to hand control over to artificial intelligence? Before that happens, doctors will probably want to see more solid proof that a computer’s predictions can improve health outcomes.

Some fear that AI diagnosis may backfire, encouraging doctors to overdiagnose and overtest patients. Even if the algorithms work well, there’s the question of how to integrate them seamlessly into clinical practice. Doctors, notoriously overworked, aren’t likely to want to add yet more items to their checklist.

Chase thinks that artificially intelligent diagnostics will end up being integrated right into databases of electronic health records, so that seeking machine insights becomes as routine as getting hold of a patient’s data.

“For physicians to delegate tasks to an AI, they must first admit to being occasionally wrong“

Apps that offer diagnostic help already exist, like Isabel, which doctors can run on Google Glass in order to keep their hands free. But Chase says this approach is unpopular, as doctors must spend time inputting patient data to use them. AI diagnostics will only take off when it imposes no additional time pressure.

There are social roadblocks, too, says Leo Anthony Celi, a doctor at the intensive care unit of the Beth-Israel Deaconess Medical Center in Boston. Down the line, Celi thinks, doctors will function more “like the captain of a ship”, delegating most daily tasks either to machines or to highly trained nurses, medical techs and physician’s assistants. For that system to succeed, doctors must first cede some control, admitting that the machine can perform better than them in some domains. That’s a tough ask in a career in which everyone from medical school professors to patients expects that doctors will always have the right answers.

Ultimately, there needs to be a cultural shift toward respect for big data and AI’s potential in medicine, argues Celi. Only then can we let machines and humans do what each does best.

“No one can really replace doctors’ ability to talk to patients,” he says. “Doctors should focus on what they do better, which is talking to patients and eliciting their values and their advance directives, and leave it up to the machine to make the complex decisions. We’re not really good at it.”

Getting our money’s worth Artificial intelligence may have a lot to offer in healthcare, but exploiting it means handing over troves of medical data to tech companies. How do we ensure that those transfers are a good deal for the public? As the recent deal between Google DeepMind and the UK’s National Health Service shows, it’s not just the quantity of patient data that matters, but its quality. NHS experts have spent a lot of time and money building and tending to the database given to Google. It’s not clear that the NHS will get that time and money back. Richard French, legal director at Digital Catapult, a non-profit R&D centre in London, says that the deal may not be the best one for the taxpayer. “One would have expected that Google would pay for access to the records in some form or another.” If there was no upfront payment, Google could have told the NHS that any commercial product based on the research would be available to it at a discount, he says. Hal Hodson

This article appeared in print under the headline “Medicine by machine”