Artificial intelligence (AI)-enabled devices are significantly changing how older adults are receiving medical care. Doctors and nurses are finding that AI can save time, improve workflows, and support faster decisions. This may allow your health team to care for more patients on a daily basis. Adults 50 and older are part of this sea change in medicine. AI is reshaping how individuals prepare for and participate in their care.
Researchers at Rutgers Robert Wood Johnson Medical School have found that an AI-enabled early warning system helped identify hospitalized patients at risk of rapid clinical decline sooner, contributing to fewer deaths among high-risk patients. The study, which was published in the New England Journal of Medicine, evaluated outcomes among 23,132 high-risk patients at 11 hospitals. Deaths among high-risk patients fell from 23.1% to 18.6% following implementation of the AI-enabled early warning system, representing an 18% reduction in the risk-adjusted odds of in-hospital death.
Hospitalized patients can deteriorate quickly, often before obvious warning signs become apparent. Researchers evaluated the Epic Deterioration Index (EDI), an AI-enabled tool that continuously analyzes information already captured in the electronic health record, including vital signs, laboratory results, and nursing assessments. The system recalculates risk scores every 15 minutes and automatically alerts rapid response teams when patients reach the highest-risk category.
“Our goal was to identify patients earlier, before they reached a point where intervention becomes much more difficult,” said study investigator Dr. Thomas Nahass, who is an assistant professor of Medicine at Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey. “The deterioration index gives us an earlier point in time. If we can get a critical care eye on the patient sooner, we can change the course of their outcome.”
AI changing how diabetes is managed as well as cancer
Research is now underway at Washington State University to help medical practitioners understand how AI can assist diabetes patients with glucose tracking and follow-up care. “All Washingtonians deserve access to the best tools we have, and our rural clinics have not been receiving the investments they need to fully leverage the promise of artificial intelligence,” said study investigator Anna Zamora-Kapoor, who is an associate professor in both the Department of Sociology and the Department of Medical Education and Clinical Sciences, Pullman, Washington. “It’s important that we’re leveling the playing field, and these projects show that with a very small investment, we can improve rural health in Washington state.”
AI is now changing how tumors are classified. They are no longer staged based on their primary tumor such as breast cancer or prostate cancer. Instead, it is from single cell data. A multinational team of researchers has developed and tested a new AI tool to better characterize the diversity of individual cells within tumors, opening doors for more targeted therapies for patients.
"Our study is the first time that single cell data have been able to simplify this continuum of cell states into a handful of meaningful archetypes through which diversity can be analyzed to find meaningful associations with spatial tumor growth and metabolomic signatures,” said Smita Krishnaswamy, who is an associate professor of computer science and genetics at Yale School of Medicine, New Haven, Connecticut.
Tumors aren't made of just one cell type. They're a mix of different cells that grow and respond to treatment in different ways. This diversity, or heterogeneity, makes cancer harder to treat and can in turn lead to worse outcomes, especially in triple negative breast cancer.
“Heterogeneity is a problem because currently we treat tumors as if they are made up of the same cell. This means we give one therapy that kills most cells in the tumor by targeting a particular mechanism. But not all cancer cells may share that mechanism. As a result, while the patient may have an initial response, the remaining cells can grow and the cancer may come back,” said study investigator Christine Chaffer with the Garvan Institute of Medical Research, Sydney, Australia.
The team developed and trained a new AI tool called AAnet that can detect patterns in data of individual cells within tumors. They used the AI tool to uncover patterns in the level of gene expression of individual cells within tumors to derive new groups. The team focused on human models of triple-negative breast cancer and human samples of ER positive, HER2 positive and triple-negative breast cancer.
They identified five different cancer cell groups within a tumor, with distinct gene expression profiles that indicated vast differences in cell behavior. The researchers say the use of AAnet to characterize the different groups of cells in a tumor according to their biology opens doors for a paradigm shift in how we treat cancer.
John Schieszer is an award-winning national journalist and radio and podcast broadcaster of The Medical Minute. He can be reached at .
John Schieszer is an award-winning national journalist and radio and podcast broadcaster of The Medical Minute.
