Validation and Bias testing of AI in Radiology

With the proliferation of artificial intelligence (AI) applications in radiology, the healthcare industry is witnessing an unprecedented surge in the number of available AI algorithms. As of the current landscape,more than 200 AI algorithms cater to various aspects of radiological imaging and diagnostics. In this dynamic scenario, the imperative for hospitals and healthcare institutions lies in the meticulous validation and bias testing of these AI algorithms to ensure their reliability, accuracy, and ethical deployment.

We will present how hospitals can use a platform based approach to perform a retrospective valdation and bias testing. The sheer diversity in AI models necessitates a systematic approach to evaluate their performance within the specific context of each hospital's patient data. Validation processes not only verify the technical robustness of the algorithms but also assess their generalizability and applicability across diverse patient demographics.