AI Could Improve Healthcare for All ― Unless It Doesn't

Jeremy Hsu

September 19, 2019

You could be forgiven for thinking that AI will soon replace human physicians based on headlines such as "The AI Doctor Will See You Now," "Your Future Doctor May Not Be Human," and "This AI Just Beat Human Doctors on a Clinical Exam." But experts say the reality is more of a collaboration than an ousting: Patients could soon find their lives partly in the hands of AI services working alongside human clinicians.

There is no shortage of optimism about AI in the medical community. But many also caution the hype surrounding AI has yet to be realized in real clinical settings. There are also different visions for how AI services could make the biggest impact. And it's still unclear whether AI will improve the lives of patients or just the bottom line for Silicon Valley companies, health care organizations, and insurers.

"I think that all our patients should actually want AI technologies to be brought to bear on weaknesses in the health care system, but we need to do it in a non-Silicon Valley hype way," says Isaac Kohane, a biomedical informatics researcher at Harvard Medical School.

If AI works as promised, it could democratize health care by boosting access for underserved communities and lowering costs — a boon in the United States, which ranks poorly on many health measures despite an average annual health care cost of $10,739 per person. AI systems could free overworked doctors and reduce the risk of medical errors that may kill tens of thousands, if not hundreds of thousands, of U.S. patients each year. And in many countries with national physician shortages, such as China where overcrowded urban hospitals' outpatient departments may see up to 10,000 people per day, such technologies don't need perfect accuracy to prove helpful.


"In the same way that technologies can close disparities, they can exacerbate disparities," says Jayanth Komarneni, founder and chair of the Human Diagnosis Project (Human Dx), a public benefit corporation focused on crowdsourcing medical expertise. "And nothing has that ability to exacerbate disparities like AI."

In the same way that technologies can close disparities, they can exacerbate disparities. Jayanth Komarneni


But to make the most of these predictions in health care, AI can't go at it alone. Rather, humans still must help make decisions that can have major health and financial consequences. Because AI systems lack the general intelligence of humans, they can make baffling predictions that could prove harmful if physicians and hospitals unquestioningly follow them.


Humans still must help make decisions that can have major health and financial consequences.

If you follow the model blindly, says Kenneth Jung, a research scientist at the Stanford Center for Biomedical Informatics Research, "then you're hosed. Because the model is saying: 'Oh, this kid with asthma came in and they got pneumonia but we don't need to worry about them and we're sending them home with some antibiotics.'"




This extrapolation will prove difficult in other situations as well. For example, says Marzyeh Ghassemi, a computer scientist and biomedical engineer at the University of Toronto, say you have 40,000 ICU patients at Beth Israel Deaconess Medical Center — that's just one hospital in one city. "And so I have all these papers that have done predictions with this data. Does that work with another hospital in Boston? Maybe. Does it work for a hospital in another state? Would it work in another country? We don't know."

While AI models may not work in every case, Ghassemi thinks the technology is still worth exploring. "I am very much in favor of taking these models from the bench to the bedside," she says, "but with really aggressive precautionary steps."

I am very much in favor of taking these models from the bench to the bedside, but with really aggressive precautionary steps. Marzyeh Ghassemi


The consent issue comes up again when the AI model is ready for experimental clinical testing with real patients. "Do patients need to be told you're using the algorithm on them, and does it matter whether the AI is completely guiding care or partly guiding care?" Cohen asks. "There is really very little thinking on these questions."

Ghassemi also advocates for frequently auditing AI algorithms to ensure fairness and accuracy across different groups of people based on ethnicity, gender, age, and health insurance. That's important given how AI applications in other fields have already shown that they can easily pick up biases.


Some regulatory agencies are rethinking how to assess health care AI. In April, the U.S. Food and Drug Administration (FDA) released a discussion paper to get public feedback about how to update the relevant regulatory review. "What we are continuously trying to do here is get back to our goal of giving people access to technologies, but we're also realizing that our current methods don't quite work well," says Bakul Patel, director for digital health at the FDA. "That's why we need to look at a holistic approach of the whole product life cycle."

In addition to issues surrounding access, privacy, and regulations, it also isn't clear just who stands to benefit the most from AI health care services. There are already health care disparities: According to the World Bank and the World Health Organization, half of the globe's population lacks access to essential health care services and nearly 100 million people are pushed into extreme poverty by health care expenses. Depending on how it is deployed, AI could either improve these inequalities, or make them worse.

"A lot of the AI discussion has been about how to democratize health care, and I want to see that happening," says Effy Vayena, a bioethicist at the Federal Institute of Technology in Switzerland.

"If you just end up with a fancier service provision to those who could afford good health care anyway," she adds, "I'm not sure if that's the transformation we're looking for."


In August 2018, Moorfields Eye Hospital in the United Kingdom and DeepMind. the London-based AI lab owned by Google's parent company Alphabet, showed that they had successfully trained an AI system to identify more than 50 eye diseases in scans, which matched the performance of leading experts. Similarly broad ambitions drove the San Diego and Guangzhou study that trained AI to diagnosis common ailments among children. The latter wasn't as good at diagnosing pediatric diseases compared to senior physicians, but it did perform better than some junior physicians.

Such AI systems may not need to outperform the best human experts to help democratize health care, but simply expand access to current medical standards. Still, so far, many proposed AI applications are focused on improving the current standard of care rather than spreading affordable health care around, Cohen says: "Democratizing what we already have would be a much bigger bang for your buck than improving what we have in many areas."

Accenture, a consulting firm, predicts that top AI applications could save the U.S. economy $150 billion per year by 2026. But it's unclear if patients and health care systems supplemented by taxpayer dollars would benefit, or if more money would simply flow to the tech companies, health care providers, and insurers.


"The question of who is going to drive this and who is going to pay for this is an important question," says Kohane. "Something a bit hallucinatory about all those business plans is that they think they know how it will work out."


There is another AI opportunity that could improve the quality of care while still leaving most medical diagnoses in the hands of doctors. In his 2019 book "Deep Medicine," Eric Topol, director and founder of the Scripps Research Translational Institute, talks about creating essentially a supercharged medical Siri — an AI assistant to take notes about the interactions between doctors and their patients, enter those notes in electronic health records, and remind physicians to ask about relevant parts of the patient's history.

"My aspiration is that we decompress the work of doctors and get rid of their data clerk role, help patients take on more responsibility, and key up the data so it doesn't take so long to review things," Topol says.

That "never-forgetful medical assistant or scribe," says Kohane, would require AI that can automatically track and transcribe multiple voices between physicians and patients. He supports Topol's idea, but adds that most AI applications in development don't seem to be focused on such assistants. Still, some companies such as Saykara and DeepScribe have developed services along these lines, and even Google teamed up with Stanford University to test a similar "digital scribe" technology.






"In the same way that multiple human professionals might look at your case in the future, there is no reason why multiple AI couldn't do it," Komarneni says.

As doctors wait for their AI helpers, crowdsourcing projects like Human Dx "could definitely lead to improved diagnostics or even improved recommendations for therapy," says Topol, who coauthored a 2018 study on a similar platform called Medscape Consult. The paper concluded collective human intelligence could be a "competitive or complementary strategy" to AI in medicine.


"There are things that machines will never do well, and then others where they'll be exceeding what any human can do," Topol says. "So when you put the two together it's a very powerful package."
