Artificial intelligence has major implications for medicine. Proponents say the technology holds great potential in predicting drug interaction, infection risk factors—even in cancer diagnoses. But how can scientists use AI to make differences in patient outcomes? Two Penn researchers offer a plan that focuses on adherence to high standards and strong regulation.
Ravi Parikh is a fellow in hematology and oncology at the Perelman School of Medicine, and Amol Navathe is a senior fellow at the Leonard Davis Institute of Health Economics and Wharton professor of health policy and medicine. Their paper titled “Regulation of Predictive Analytics in Medicine” was published in the journal Science.
“What’s new here is that we have an expanse—troves and troves of data that are being collected as a byproduct of providing care—in the electronic health record,” says Navathe. “All of a sudden, we have the techniques to be able to harness all of that data to try to improve clinical care.”
“The nature of some of these algorithms is that they can generate predictions in real time, in automated fashion. I think about decision rules that I use in clinical practice that require often tens or dozens of variables of manual input, taking up time during which I could be talking to patients,” explains Parikh. “One of the promises of these algorithms is that they can provide predictions right at the point of care.”
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