Roadmap to fair AI: Revealing biases in AI models for medical imaging

Artificial intelligence and machine learning (AI/ML) technologies are constantly finding new applications across several disciplines. Medicine is no exception, with AI/ML being used for the diagnosis, prognosis, risk assessment, and treatment response assessment of various diseases. In particular, AI/ML models are finding increasing applications in the analysis of medical images. This includes X-ray, computed tomography, and magnetic resonance images. A key requirement for the successful implementation of AI/ML models in medical imaging is ensuring their proper design, training, and usage. In reality, however, it is extremely challenging to develop AI/ML models that work well for all members of a population and can be generalized to all circumstances.