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SALT LAKE CITY — Dr. AI is setting up practice in a medical setting near you. And experts say your health care is likely to improve in multiple ways with that new attention to detail. But in some areas, you might want to steer clear.
Artificial intelligence is quickly becoming a staple in growing segments of health care, but it's not ready in others. Still, experts say you need not worry that you'll lose the personal touch if you've been getting that from a medical provider: Humans are as important as ever in the practice of medicine. You might even find care providers have more time to address your needs.
The National Institutes of Health notes artificial intelligence tools are driving widespread change across medical disciplines, including research, diagnosis and treatment. Concurrent advancements in computing power and the proliferation of massive, health-related data sets are setting the stage for new approaches in the research field as scientists increasingly employ AI software and its powerful information processing capabilities to advance their work at an increasingly rapid pace.
"I am excited about the technology," said attorney Daniel J. Gilman, senior scholar at the International Center for Law and Economics, a nonpartisan, nonprofit research center based in Portland, Oregon. "I think we've seen that as long as it is introduced and used in a careful and responsible fashion, AI seems to have tremendous promise."
From experiment to problem solver
Dr. Yves Lussier is both a physician and engineer — and an unabashed AI enthusiast. At the University of Utah School of Medicine, he's department chairman of biomedical informatics — the founding department of that field in the U.S. and maybe the world, dating from the late 1950s, he said.
Lussier traces the roots of AI to the 1940s, when neural networks were developed to "reason with uncertainty." Later, AI advanced to reason with certainty. The pace of each AI breakthrough has been faster than the one before. By the mid-1970s, software from Stanford could reason with both certainty and uncertainty — "expert systems," he said. Since, Depp learning (15 years ago) and "transformers" (seven years ago) have led to the emerging conversational AIs called "generative AI," such as ChatGPT.
AI types abound. Most people don't realize the voice recognition that's an open sesame to your bank account is AI. What Lussier calls the "game changer" came seven years ago with generative AI, which can be prompted to create texts, images, videos and other data. You can push a transcript into a generative AI and retrieve plainspoken words or technical terms depending on your audience.
The biggest impact, perhaps, is helping solve problems that have been largely intractable.
Dr. Nathan Blue is an obstetrician and assistant professor at the University of Utah School of Medicine's Department of Obstetrics and Gynecology. Blue has been involved with research efforts for over a decade, working to develop new clinical diagnostic strategies that can identify early signs of pregnancy complications that arise from deficiencies in placental material.
Those deficiencies, Blue said, can lead to fetal growth restrictions, complications involving bleeding, preeclampsia and stillbirths. The traditional research strategies to quantify risk associated with placental deficiencies have a lot of pitfalls, he said, and can be crude and inflexible.
New research techniques that incorporate AI systems are showing promise for overcoming some of the inefficiencies of previous strategies and could lead to new clinical practices that will, Blue said, lower stress for expectant mothers, help reduce uncertainty about medical intervention decisions and lead to better use of resources.
"In the last couple of years, we've started working with the bioinformatics and genomics group here at the U.," Blue said. "These senior thought leaders and investigators have helped us leverage more computationally advanced approaches, including artificial intelligence, to better quantify risk."
Part of the research work includes applying AI tools to large data troves, including anonymized genomic profiles of more than 10,000 obstetrics patients, and zeroing in on diagnostic markers that may become part of new clinical applications to help more accurately predict future at-risk pregnancies.
"On the research and investigation side, what is really exciting about what the AI-based tools and approach can offer is, until now, we've been trying different versions of the same thing," Blue said. "Using pretty old-fashioned tools to find factors, but really we're mucking about in the same sandbox, so to speak.
"What I'm super excited about in the AI-based strategy is it's helping us bypass a lot of the pitfalls to analysis but boosting how we conceptualize how we use information. In that sense, the progress is really accelerating risk for pregnancy work and the application and accuracy of those tools is better than what we were getting before."
Professor Xiaondong Ma from the University of Utah School of Medicine's Department of Radiology and Imaging Sciences is a member of the Medical Imaging and Computational Analysis Lab, a research team working to develop advanced techniques, including AI in image acquisition, analysis and quantification for clinical and research applications.
Among other projects, he and his team are investigating vascular issues — particularly how abnormalities in the carotid artery could serve as indicators of more serious vascular pathologies.
Ma said analyzing images captured by MRI and/or CT scans has traditionally been a manual, time-consuming process. Thanks to an AI-powered, semiautomatic image analysis technique being developed by the Medical Imaging and Computational Analysis Lab, the work to develop new diagnostic strategies has accelerated.
Another medical imaging project that's leveraging AI-based image analysis tools is looking at the connection between calcification that occurs in the brain and its relationship to the ravages of aging, he said.
"We have the potential to predict vascular disease and diseases associated with aging, like Alzheimer's," Ma said. "Our hope is that AI can help us screen these images and define which patients may be of high risk."
Workload triage
Women who've had a pap smear are already part of AI's story in medicine. AI has been used for 30 years to sort through millions of exams annually to determine which need special attention, proving its worth as triage there and in radiology, among others. AI may spot the earliest signs of unhealthy tissue change that the naked eye could miss, saving time, money and suffering.
The AI is designed to have many false positives and no false negatives. "It doesn't make an error of forgetting a cancer. But it claims 10 times more often that there's cancer when there is none," which a pathology expert sorts out, Lussier said. But how fast AI runs through images makes cervical cancer screening manageable and affordable.
Lussier said it took a decade or more to design that kind of artificial intelligence long years ago. Now, given the pace of advances, such a program could probably be created in weeks or months.
Would a redesign improve results? "No, it's highly accurate. But it would cost a lot less now because it would take less time and be done with better tools. That's the game changer," said Lussier. Faster design using fewer resources means lower costs, accessible for more users within industries like health care, providing greater benefit for consumers.
