by Adina Bresge, University of Toronto
Credit: Pixabay/CC0 Public Domain
Muhammad Mamdani understands why people are wary of artificial intelligence having a say in their health care—but he’s even more concerned about the patients who are waiting to benefit from the potentially life-saving benefits of AI-assisted medicine.
As vice-president, data science and advanced analytics at Unity Health Toronto, Mamdani has overseen the implementation of more than 50 AI-powered solutions into clinical practice—from an early warning system that uses electronic medical records to predict a patient’s risk of death or requiring intensive care, to a brain-bleed detection tool that can help fast-track access to critical treatment.
And he says there’s more to come in 2024.
“I hope to see more AI being used for clinical decision making,” say Mamdani, who is a professor in the department of medicine in the University of Toronto’s Temerty Faculty of Medicine and the director of the Temerty Center for Artificial Intelligence Research and Education in Medicine (T-CAIREM).
Yet, despite AI’s potential to transform patient care, it isn’t a cure-all for the underlying problems in Canada’s health system, warns Mamdani, who holds cross-appointments in U of T’s Leslie Dan Faculty of Pharmacy and the Institute of Health Policy, Management and Evaluation at the Dalla Lana School of Public Health.
Mamdani recently spoke to U of T News about how AI will—and won’t—shape health care in 2024.
How do you expect AI will transform health care in 2024?
For the past few years, we’ve been in this era of AI hype in health care. A lot of talk, some people doing a few small things here and there, but not really a big splash—and I’m not sure we’ll see a big splash in 2024. A lot of organizations are actively getting into this space, but I would say we’re still at least a few years away from seeing really, really big changes. Instead, I think we’ll see a more gradual adoption of AI in health care.
In 2024, I hope to see more AI being used for clinical decision making. Right now, we’re seeing it used more for non-clinical or administrative tasks. For example, quite a few primary-care clinics and outpatient clinics are using AI scribes that can “listen” to a conversation between a doctor and a patient, transcribe the visit and provide a really good summary note.
[Doctors] are notorious for not writing everything down, and that’s very unfortunate because medicine is very data- and information-driven. When a doctor is talking with a patient, they’re focused on the patient—as they should be—but when the patient leaves, they might have forgotten many of the things that were discussed or didn’t have time to write things down. Then you have an imperfect data set the next time around.
We’re also starting to see tools that can take these transcriptions to suggest diagnoses or recommend medications and, with the doctor’s OK, send prescriptions to the pharmacy.
This coming year, [at Unity Health], we’re working on creating a multimodal data environment that incorporates not only clinical data, but also medical imaging data and waveform data from monitors and ventilators that we’re able to access in real time. For example, you could go into the ICU and constantly ingest data from ventilators to understand if a patient is going to have trouble breathing in the next 20 minutes.
Provided by University of Toronto
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