The development of predictive AI tools in healthcare shows tremendous promise in accelerating more accurate diagnoses and improving the safety and quality of healthcare.
However, what’s been lacking is a standard way to evaluate whether or not an AI tool will do what it says it does. As a result, health systems are often left on their own to develop a way to evaluate competing solutions from scratch. It is easy to spend precious hours researching available products, as there are many technical and logistical components to understand.
We created this checklist together with leading clinicians and informaticists detail the 10 consistent components every predictive tool needs to have.