The Boundaries of Fair AI in Medical Image Prognosis: A Causal Perspective

2025-10-09

AI Summary

Social group bias in medical imaging prognosis—specifically time-to-event prediction—has been largely overlooked; existing fairness research focuses predominantly on diagnostic tasks, with no systematic evaluation for prognostic settings. Method: We propose FairTTE, the first fairness assessment framework tailored to time-to-event prediction, supporting multimodal imaging and diverse prognostic tasks. Leveraging a causal perspective, we model the structural mechanisms of multi-source biases in imaging data, establishing a fine-grained, task-adapted fairness analysis paradigm that integrates survival analysis, causal inference, and fair machine learning to identify, quantify, and mitigate bias pathways. Contribution/Results: Large-scale experiments reveal pervasive group-level biases across imaging modalities; state-of-the-art debiasing methods exhibit insufficient robustness under distributional shift; and performance disparities strongly correlate with deep causal bias sources. This work highlights and advances the development of robust, fair prognostic models.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

As machine learning (ML) algorithms are increasingly used in medical image analysis, concerns have emerged about their potential biases against certain social groups. Although many approaches have been proposed to ensure the fairness of ML models, most existing works focus only on medical image diagnosis tasks, such as image classification and segmentation, and overlooked prognosis scenarios, which involve predicting the likely outcome or progression of a medical condition over time. To address this gap, we introduce FairTTE, the first comprehensive framework for assessing fairness in time-to-event (TTE) prediction in medical imaging. FairTTE encompasses a diverse range of imaging modalities and TTE outcomes, integrating cutting-edge TTE prediction and fairness algorithms to enable systematic and fine-grained analysis of fairness in medical image prognosis. Leveraging causal analysis techniques, FairTTE uncovers and quantifies distinct sources of bias embedded within medical imaging datasets. Our large-scale evaluation reveals that bias is pervasive across different imaging modalities and that current fairness methods offer limited mitigation. We further demonstrate a strong association between underlying bias sources and model disparities, emphasizing the need for holistic approaches that target all forms of bias. Notably, we find that fairness becomes increasingly difficult to maintain under distribution shifts, underscoring the limitations of existing solutions and the pressing need for more robust, equitable prognostic models.

Problem

• Addresses fairness gaps in medical image prognosis beyond diagnosis tasks
• Introduces framework to assess bias in time-to-event prediction models
• Identifies pervasive bias across imaging modalities using causal analysis

Innovation

• First framework for fairness in time-to-event prediction
• Integrates causal analysis to quantify bias sources
• Evaluates fairness robustness under distribution shifts