We test the capabilities of each Gen AI model automatically using another GenA models to create test cases and scenarios that effectively create a robust framework to statistically test the accuracy of the models over time, as well as vary the input conditions. This constant testing loop, ensures we have proper data integrity flow built into the platform from the very start. The Gen AI loop gives us a much higher ratio of test conditions that we could ever reasonably build with an traditional automated test harness.
The automated GenAI test framework allows us to build a robust database over time proving the accuracy, and showing he we respond to an extremely wide variety of difficult, ambiguous edge-cases, that would be hard and time consuming to replicate manually. It allows us to test for bias - something that is notoriously difficult to do.
Although Gen AI as we've seen can automate much of the testing process, our human experts still control the overall process, and human knowledge is still necessary to interpret the results, as well as making decisions about the results.
While much of the focus with Gen AI has been driven by the business logic of replacing high cost employees, we see that GenAI will create tremendous value to fundamentally improve patient outcomes and experiences as well as the inevitable streamlining of operations in underserved markets. Such a process allows us to focus and leave our healthcare teams focusing almost completely on patient welfare and outcomes.