AI alignment in medical imaging: Unveiling hidden biases through counterfactual analysis

Machine learning (ML) systems for medical imaging have demonstrated remarkable diagnostic capabilities. However, their susceptibility to learning spurious correlations with sensitive attributes poses significant risks to fairness and safety. In this paper, we introduce a novel statistical framework to evaluate the dependency of medical imaging ML models on sensitive attributes, such as demographics. Our method leverages the concept of counterfactual invariance, measuring the extent to which a model’s predictions remain unchanged under hypothetical changes to sensitive attributes. We present a practical algorithm that combines conditional latent diffusion models with statistical hypothesis testing to identify and quantify such biases without requiring direct access to counterfactual data. On synthetic benchmarks, our framework correctly identifies 96.1% of biased models while producing false alarms on 22.0% of fair models. On two real-world chest X-ray datasets, CheXpert and MIMIC-CXR, it detects bias at average rates 96.3%, 95.7% across all diagnostic tasks when models are strongly biased, and its average false alarm rate drops to 15.3%, 14.7%, respectively, for the least biased models, consistently outperforming existing fairness baselines and demonstrating strong alignment with counterfactual fairness principles.Author summary: Machine learning (ML) systems are increasingly used in medical imaging for diagnostic purposes. However, these systems can learn biases from training data, leading to disparities in clinical outcomes, particularly across demographic groups. Our work addresses this critical issue by introducing a novel statistical framework that tests whether an ML model’s predictions change when sensitive attributes—such as self-reported race or gender—are altered. We achieve this by using latent conditional diffusion models to create realistic image variations. These tools allow us to measure a model’s dependency on sensitive attributes and assess its fairness. We validate our method on synthetic and real-world medical imaging datasets, demonstrating its effectiveness and alignment with counterfactual fairness principles. This approach represents an important step towards building trustworthy and unbiased AI systems in healthcare.