Mitigating Bias in Opportunistic Screening for MACE with Causal Reasoning.

Mitigating population drift is vital for developing robust AI models for clinical use. While current methodologies focus on reducing demographic bias in disease predictions, they overlook the significant impact of chronic comorbidities. Addressing these complexities is essential to enhance predictive accuracy and reliability across diverse patient demographics, ultimately improving healthcare outcomes. We propose a causal reasoning framework to address selection bias in opportunistic screening for 1-year composite MACE risk using chest X-ray images. Training in high-risk primarily Caucasian patients (43% MACE event), the model was evaluated in a lower-risk emergency department setting (12.8% MACE event) and a relatively lower-risk external Asian patient population (23.81% MACE event) to assess selection bias effects. We benchmarked our approach against a high-performance disease classification model, a propensity score matching strategy, and a debiasing model for unknown biases. The causal+confounder framework achieved an AUC of 0.75 and 0.7 on Shift data and Shift external, outperforming baselines, and a comparable AUC of 0.7 on internal data despite penalties for confounders. It minimized disparities in confounding factors and surpassed traditional and state-of-the-art debiasing methods. Experimental data show that integrating causal reasoning and confounder adjustments in AI models enhances their effectiveness. This approach shows promise for creating fair and robust clinical decision support systems that account for population shifts, ultimately improving the reliability and ethical integrity of AI-driven clinical decision-making.