Some of the biggest names in AI are raising questions about whether innovation is moving faster than our ability to manage its risks.
Interestingly, healthcare has been dealing with this tension for years.
AI deployments in health systems go through extensive security, privacy, compliance and technology vetting. In our experience at Inference Analytics AI, that can even mean customers are not immediately using the newest models available.
That may seem like a disadvantage. But not being on the bleeding edge can also reduce exposure to risks that haven't yet been fully understood.
The challenge is finding the right balance between responsible vetting and unnecessary friction.
I discuss in this video what our experience deploying AI in healthcare has taught me about that balance, and why moving a little slower can sometimes make AI adoption safer.
