Thorough testing and caution in AI adoption in healthcare

In the realm of healthcare, gathering filtered customer data from health care providers presents a challenge. Additionally, there is a need for data on the health care provider experience, which further complicates matters. Healthcare is progressing in AI adoption, although not as advanced as financial services, there is a catching up factor. There is a natural inclination to proceed cautiously by thoroughly testing technology before implementation, as emphasized by a medical director at Duke University Health Systems who insists on using AI models older than two years for thorough testing. This cautious approach is embedded in the American healthcare system due to an aversion towards accumulating more technical debt.