AI governance in health care

Across labs and clinics worldwide, scientists are harnessing artificial intelligence to improve diagnostics, personalize treatment pathways and reimagine the modes of care delivery we once thought fixed. From AI-enhanced imaging that catches early-stage tumors to predictive analytics that anticipate disease progression, the scientific community is delivering unprecedented innovations. In the long-term, novel AI-enabled therapies and groundbreaking insights at the molecular level seem within reach, as well.

As scientists work wonders with AI, they also enjoy widespread credibility in society. This is remarkable, given historically low public confidence in many institutions. Consider that 76% of Americans say they have a fair amount or a great deal of confidence in scientists to act in the public interest — a higher rating than for elected officials, journalists or business leaders. Separate polling has found that health care providers, researchers and Centers for Disease Control and Prevention rank among the most trusted sources of public health information.

So, when we talk about AI governance in domains as sensitive and high stakes as health care and life sciences, there is a tremendous opportunity to advance the use of AI with professionals who are already trusted.

How to maintain trust in health AI

While general trust in health care professionals is high, trust in AI is relatively low. Whether due to health already being a heavily regulated industry, or the recognition by health care workers that regulations support increased trust, rules and best practices for the use of AI in health care is burgeoning.

Customers have shown openness to AI-powered health care and advice — but they demand human oversight, transparency and good user experience. Biased and poorly implemented AI systems have already resulted in lawsuits, fines and reputational impacts for major health providers.