Consider the case of Vera, a 60 year old woman of Asian descent with a history of hypertension and asthma, entering the doctor's office with symptoms consistent with a diagnosis of pneumonia. Her primary care physician must diagnose and treat the acute illness, but also manage risk for chronic diseases such as heart attack, stroke, renal failure, and osteoporosis. Ideally, treatment decisions for Vera are guided by risk stratification to decide if to treat, and evidence based selection of how to treat. Although there is research devoted to learning from similar patients to figure out optimal treatments, not enough work is done for effective risk-stratification–which can guide the decision to take action (and on whom). As a result, today’s health system, and Vera's care, remains reactive and suboptimal.

Imagine how Vera’s experience would change if we could predict risks of specific events and take proactive action.

We are working on a set of efforts, which are collectively referred to as the Stanford Medicine Program for Artificial Intelligence in Healthcare, with the mission of bringing AI technologies to the clinic, safely, cost-effectively and ethically. See brochure.