Treatments become understandable to patients
“Encoding biology creates algorithms that are deeply explainable because they reveal why a person is at risk, how to reduce that risk, and how much each person will benefit from specific actions to reduce risk,” Dr. Ference said.
A real-world, randomized trial to confirm that the information from causal AI can reduce the risk of CAD is expected to start in 2023, but Dr. Ference thinks that causal AI for managing CAD risk, independent of this planned trial, is essentially inevitable. PGS, which he thinks will be performed routinely in all individuals within 10 years, is only likely to improve. He foresees large advantages of this form of personalized medicine.
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Dr. Ami Bhatt
Ami Bhatt, MD, chief innovation officer for the American College of Cardiology, Washington, agreed, seeing a direct relationship between precision health as the pathway to improvements in population health.
By explaining risk factors in terms of mechanisms and specific goals to ameliorate these risks, it “engages our patients with agency,” said Dr. Bhatt. She suggested that the information provided by causal AI has the potential to empower patients while creating a collaborative approach with clinicians to CAD prevention.
With patient-specific information provided in the context of the disease biology, “you increase the sense of transparency,” Dr. Bhatt said.
She suggested this direction of research is wholly consistent with initiatives such as those from the World Health Organization to improve precision medicine as a step toward equipping patients to manage their own health.
Dr. Ference reported financial relationships with Amgen, AstraZeneca, CiVi Pharma, Daiichi Sankyo, DalCOR, Esperion, Eli Lilly, Ionis Pharmaceuticals, KrKA, Medicines Company, Merck, Mylan, Novo Nordisk, Novartis, and Sanofi, and Viatris. Dr. Bhatt reported no potential conflicts of interest.