Artificial intelligence has come to medicine. Are patients being put at risk?
Health products powered by artificial intelligence are streaming into our lives, from virtual doctor apps to wearable sensors and drugstore chatbots.
IBM boasted that its AI could “outthink cancer.” Others say computer systems that read X-rays will make radiologists obsolete. AI can help doctors interpret MRIs of the heart, CT scans of the head and photographs of the back of the eye, and could potentially take over many mundane medical chores, freeing doctors to spend more time talking to patients, said Dr. Eric Topol, a cardiologist and executive vice president of Scripps Research in La Jolla.
“There’s nothing that I’ve seen in my 30-plus years studying medicine that could be as impactful and transformative” as AI, Topol said. Even the Food and Drug Administration ― which has approved more than 40 AI products in the last five years ― says “the potential of digital health is nothing short of revolutionary.”
Yet many health industry experts fear AI-based products won’t be able to match the hype. Some doctors and consumer advocates fear that the tech industry, which lives by the mantra “fail fast and fix it later,” is putting patients at risk ― and that regulators aren’t doing enough to keep consumers safe.
Early experiments in AI provide a reason for caution, said Mildred Cho, a professor of pediatrics at Stanford’s Center for Biomedical Ethics.
Systems developed in one hospital often flop when deployed in a different facility, Cho said. Software used in the care of millions of Americans has been shown to discriminate against minorities. And AI systems sometimes learn to make predictions based on factors that have less to do with disease than the brand of MRI machine used, the time a blood test is taken or whether a patient was visited by a chaplain.
In one case, AI software incorrectly concluded that people with pneumonia were less likely to die if they had asthma ― an error that could have led doctors to deprive asthma patients of the extra care they need.
“It’s only a matter of time before something like this leads to a serious health problem,” said Dr. Steven Nissen, chairman of cardiology at the Cleveland Clinic.
Medical AI, which pulled in $1.6 billion in venture capital funding in the third quarter alone, is “nearly at the peak of inflated expectations,” concluded a July report from research company Gartner. “As the reality gets tested, there will likely be a rough slide into the trough of disillusionment.”
That reality check could come in the form of disappointing results when AI products are ushered into the real world. Even Topol, the author of “Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again,” acknowledges that many AI products are little more than hot air.
Experts such as Dr. Bob Kocher, a partner at the venture capital firm Venrock, are blunter. “Most AI products have little evidence to support them,” Kocher said. Some risks won’t become apparent until an AI system has been used by large numbers of patients. “We’re going to keep discovering a whole bunch of risks and unintended consequences of using AI on medical data,” Kocher said.
None of the AI products sold in the U.S. have been tested in randomized clinical trials, the strongest source of medical evidence, Topol said. The first and only randomized trial of an AI system ― which found that colonoscopy with computer-aided diagnosis found more small polyps than standard colonoscopy ― was published online in October.
Few tech start-ups publish their research in peer-reviewed journals, which allow other scientists to scrutinize their work, according to a January article in the European Journal of Clinical Investigation. Such “stealth research” ― described only in press releases or promotional events ― often overstates a company’s accomplishments.
And although software developers may boast about the accuracy of their AI devices, experts note that AI models are mostly tested on computers, not in hospitals or other medical facilities. Using unproven software “may make patients into unwitting guinea pigs,” said Dr. Ron Li, medical informatics director for AI clinical integration at Stanford Health Care.
AI systems that learn to recognize patterns in data are often described as “black boxes” because even their developers don’t know how they reached their conclusions. Given that AI is so new ― and many of its risks unknown ― the field needs careful oversight, said Pilar Ossorio, a professor of law and bioethics at the University of Wisconsin-Madison.
Yet the majority of AI devices don’t require FDA approval. “None of the companies that I have invested in are covered by the FDA regulations,” Kocher said.
Legislation passed by Congress in 2016 ― and championed by the tech industry ― exempts many types of medical software from federal review, including certain fitness apps, electronic health records and tools that help doctors make medical decisions.
There’s been little research on whether the 320,000 medical apps now in use actually improve health, according to a report on AI published Dec. 17 by the National Academy of Medicine.
The FDA has long focused its attention on devices that pose the greatest threat to patients. And consumer advocates acknowledge that some devices ― such as ones that help people count their daily steps ― need less scrutiny than ones that diagnose or treat disease.
This article originally appeared on latimes.com To read the full article and see the images, click here.
Nastel Technologies uses machine learning to detect anomalies, behavior and sentiment, accelerate decisions, satisfy customers, innovate continuously. To answer business-centric questions and provide actionable guidance for decision-makers, Nastel’s AutoPilot® for Analytics fuses:
- Advanced predictive anomaly detection, Bayesian Classification and other machine learning algorithms
- Raw information handling and analytics speed
- End-to-end business transaction tracking that spans technologies, tiers, and organizations
- Intuitive, easy-to-use data visualizations and dashboards