The clinical adoption of artificial intelligence (AI) in medical imaging faces significant hurdles, primarily due to concerns about safety, reliability, and fairness. AI models are sensitive to data distribution shifts—changes in patient populations, imaging protocols, and geographic regions—which can lead to misdiagnosis and clinical errors. Comprehensive stress testing and robust model assurance are essential, but the necessary diverse datasets are difficult to obtain. Traditional approaches rely on gathering large annotated datasets, which is costly and time-consuming, and often fail to represent underrepresented subgroups, leading to bias and reduced generalizability.
Recent advances in generative AI, particularly causal generative models, offer a promising solution. These models can synthesize highly realistic data with specific, controllable characteristics, enabling ‘what-if’ scenarios—such as simulating the presence of disease or changes in imaging protocols given real-world observations. Unlike conventional generative models that capture only correlations, causal generative AI incorporates domain knowledge and causal relationships, enabling counterfactual image generation to enhance training data, stress-test AI models, and effectively mitigate bias.
