Using AI in health care
Scientific, technological and innovative discoveries happen daily — and the surge in artificial intelligence has sparked recent widespread conversation about how professionals will use the application in various settings.
Health care is one of many settings where AI profoundly impacts the human experience. Health care already uses AI through automated monitoring systems and on-demand processing tools, and many second-generation AI applications are either approved by the Food and Drug Administration or almost there.
“Third-generation AI will provide more autonomous features, such as constantly monitoring the electronic health record and any ongoing clinical assessment protocol, diagnostic routine, treatment regimen or ongoing medical procedure, and provide on- the-fly ‘holistic’ clinician-assistance with derived-interference, which supplements data elements already accounted for by health professionals,” said Ivo Dinov, professor of computational medicine and bioinformatics and director of the biomedical informatics and data science training program at the University of Michigan.
“AI is advancing on all fronts,” he added.
Some areas of focus in health care include using foundational AI models to generate realistic synthetic medical text; using generative AI to simulate 2D, 3D and 4D medical images; and using AI to synthetically generate computer code.
“Clinical AI is related to early detection of certain pathologies, such as a tumor. Since we know how to treat many early-stage diseases, early detection is very powerful,” said Ignacio Fuentes Ribas, program director at the Jameel Clinic for Machine Learning in Health at the Massachusetts Institute of Technology.
One crucial aspect to remember is that AI is only as good as the humans behind it. The utilization of AI in health care settings isn’t intended to eliminate or reduce complete human control over health care practices; it is designed to supplement and streamline these processes.
“This is a joint effort; the physician and algorithm model works very well,” said Ribas.
However, there is a tug-of-war between the incredible potential benefits (e.g., cost-effective clinical decision support systems; extra-human clinical abilities; augmentation of medical expert knowledge with almost limitless, quick and highly effective machine storage and rational judgment) and any potential drawbacks (e.g., risks of misuse, accidental leaks of sensitive information, rare but potentially devastating AI mistakes, etc.).
Dinov said that moving forward is trivial.
“The quick technological immersion will impact everything we do (human experience), all the time (longitudinal trajectory), everywhere we go (geo-spatially) and all at once (simultaneously),” he said. “As opposed to special interests and loud emotions, collective human reasoning and effective policy incentives will provide a fertile ground for achieving maximum benefit at the lowest costs.”
However, Ribas said that AI in health care, while receiving a lot of buzz, is still a work in progress.
“We see AI all around us in our everyday lives,” he said. “Unfortunately, in health, AI is lagging. We hope that with the adoption of AI, this can help improve patient outcomes.”