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A Reality Check On Artificial Intelligence: Are Health Care Claims Overblown?
Health products powered by artificial intelligence, or AI, are streaming into our lives, from virtual doctor apps to wearable sensors and drugstore chatbots.
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, Topol said.
Even the Food and Drug Administration ― which has approved more than 40 AI products in the past 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.
Many 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.
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.
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 the 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.
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.
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.
“It’s not the main concern of these firms to submit themselves to rigorous evaluation that would be published in a peer-reviewed journal,” said Joachim Roski, a principal at Booz Allen Hamilton, a technology consulting firm, and co-author of the National Academy’s report.
“Nobody is going to be happy, including investors, if people die or are severely hurt.” Relaxing Standards At The FDA The FDA has come under fire in recent years for allowing the sale of dangerous medical devices, which have been linked by the International Consortium of Investigative Journalists to 80,000 deaths and 1.7 million injuries over the past decade.
Many of these devices were cleared for use through a controversial process called the 510(k) pathway, which allows companies to market “moderate-risk” products with no clinical testing as long as they’re deemed similar to existing devices.
The FDA cleared an AI device designed to help diagnose liver and lung cancer in 2018 based on its similarity to imaging software approved 20 years earlier.
The FDA’s pilot “pre-certification” program, launched in 2017, is designed to “reduce the time and cost of market entry for software developers,” imposing the “least burdensome” system possible.
Scott Gottlieb said in 2017 while he was FDA commissioner that government regulators need to make sure its approach to innovative products “is efficient and that it fosters, not impedes, innovation.” Under the plan, the FDA would pre-certify companies that “demonstrate a culture of quality and organizational excellence,” which would allow them to provide less upfront data about devices.
Elizabeth Warren (D-Mass.), Tina Smith (D-Minn.) and Patty Murray (D-Wash.) questioned the agency’s ability to ensure company safety reports are “accurate, timely and based on all available information.” When Good Algorithms Go Bad Some AI devices are more carefully tested than others.
Eventually, researchers realized the computer had merely learned to tell the difference between that hospital’s portable chest X-rays ― taken at a patient’s bedside ― with those taken in the radiology department.
A blog post on the DeepMind website described the system, used at a London hospital, as a “game changer.” But the AI system also produced two false alarms for every correct result, according to a July study in Nature.
For example, a doctor worried about a patient’s kidneys might stop prescribing ibuprofen ― a generally safe pain reliever that poses a small risk to kidney function ― in favor of an opioid, which carries a serious risk of addiction.
Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril
The emergence of artificial intelligence (AI) in health care offers unprecedented opportunities to improve patient and clinical team outcomes, reduce costs, and impact population health.
and outlines key considerations for moving forward. AI is poised to make transformative and disruptive advances in health care, but it is prudent to balance the need for thoughtful, inclusive health care AI that plans for and actively manages and reduces potential unintended consequences, while not yielding to marketing hype and profit motives.
- On 19. juni 2021
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