AI models utilized in healthcare must be trustworthy! But, what precisely are the dimensions of trustworthiness, and how can we quantitatively measure them?
The reported performance of AI in scientific papers often differs significantly from the performance observed in real-life deployments for a variety of reasons. These include hidden mathematical assumptions within AI models, inadequate testing, inaccurate claims about the capabilities of AI systems, and a failure to generalize effectively to out-of-distribution scenarios.
Our extensive library of validation tools, including CE/FDA certified options, are at your disposal to help you thoroughly evaluate the trustworthiness of your AI models. Our tools can assess various aspects, such as generalizability, reliability, robustness, bias, out-of-distribution behavior, and uncertainty. With our assistance, you can quantify the range of functionality that your AI models can reliably claim. We can help you discover answers to questions such as:
- What are the weaknesses of my AI model?
- In which scenarios would my AI system fail?
- How much point-testing is enough before concluding a continuous coverage of functionality for my AI algorithm?
- Is my AI algorithm robust and reliable?
- How can I generate thousands of realistic data scenarios to test generalizability of my AI algorithm?
- Is my AI algorithm fair?
- Should I be concerned about the missing values in my training data?
- How should I improve the quality of my training data before feeding it into AI?
- Where are the boundaries of trustful functionality and what is OOD for my AI?
- How should I treat my AI response to OOD scenarios?