“Companies come in promising the world and often don’t deliver,” said Bob Wachter, head of the department of medicine at the University of California, San Francisco. “When I look for examples of … true AI and machine learning that’s really making a difference, they’re pretty few and far between. It’s pretty underwhelming.”
Administrators say algorithms — the software that processes data — from outside companies don’t always work as advertised because each health system has its own technological framework. So hospitals are building out engineering teams and developing Artificial Intelligence and other technology tailored to their own needs.
But it’s slow going. Research based on job postings shows health care behind every industry except construction in adopting AI.
The Food and Drug Administration has taken steps to develop a model for evaluating AI, but it is still in its early days. There are questions about how regulators can monitor algorithms as they evolve and rein in the technology’s detrimental aspects, such as bias that threaten to exacerbate health care inequities.
“Sometimes there’s an assumption that AI is working, and it’s just a matter of adopting it, which is not necessarily true,” said Florenta Teodoridis, a professor at the University of Southern California’s business school whose research focuses on AI. She added that being unable to understand why an algorithm came to a certain result is fine for things like predicting the weather. But in health care, its impact is potentially life-changing.
Despite the obstacles, the tech industry is still enthusiastic about AI’s potential to transform health care.
“The transition is slightly slower than I hoped but well on track for AI to be better than most radiologists at interpreting many different types of medical images by 2026,” Hinton told POLITICO via email. He said he never suggested that we should get rid of radiologists, but that we should let AI read scans for them.
If he’s right, Artificial Intelligence will start taking on more of the rote tasks in medicine, giving doctors more time to spend with patients to reach the right diagnosis or develop a comprehensive treatment plan.
“I see us moving as a medical community to a better understanding of what it can and cannot do,” said Lara Jehi, chief research information officer for the Cleveland Clinic. “It is not going to replace radiologists, and it shouldn’t replace radiologists.”
Radiology is one of the most promising use cases for AI. The Mayo Clinic has a clinical trial evaluating an algorithm that aims to reduce the hours-long process oncologists and physicists undertake to map out a surgical plan for removing complicated head and neck tumors.
An algorithm can do the job in an hour, said John D. Halamka, president of Mayo Clinic Platform: “We’ve taken 80 percent of the human effort out of it.” The technology gives doctors a blueprint they can review and tweak without having to do the basic physics themselves, he said.
NYU Langone Health has also experimented with using AI in Radiology. The health system has collaborated with Facebook’s Artificial Intelligence Research group to reduce the time it takes to get an MRI from one hour to 15 minutes. Daniel Sodickson, a radiological imaging expert at NYU Langone who worked on the research, sees opportunity in AI’s ability to downsize the amount of data doctors need to review.
Covid has accelerated AI’s development. Throughout the pandemic, health providers and researchers shared data on the disease and anonymized patient data to crowdsource treatments.
Microsoft and Adaptive Biotechnologies, which partner on machine learning to better understand the immune system, put their technology to work on patient data to see how the virus affected the immune system.
“The amount of knowledge that’s been obtained and the amount of progress has just been really exciting,” said Peter Lee, corporate vice president of research and incubations at Microsoft.
