揭示医疗AI数据偏差来源，提出改进临床数据收集的实用方案
Bias by Design? How Data Practices Shape Fairness in AI Healthcare Systems
- 从真实项目中识别出历史、代表性和测量三类数据偏差
- 发现性别、年龄、社会经济地位等变量在数据中普遍存在偏差
- 适合关注医疗AI公平性的研究者与临床数据设计者参考
人工智能在医疗领域前景广阔，但其在真实临床实践中的应用仍受限，主要源于训练数据的质量与公平性问题。本文基于西班牙国家级研发项目AI4HealthyAging的经验，聚焦临床数据收集过程中的偏见检测。研究识别出多类偏差，包括历史偏差、代表性偏差和测量偏差，其影响体现在性别、性别、年龄、居住地、社会经济地位、设备类型及标注方式等多个变量上。研究最后提出提升临床问题设计与数据收集公平性与鲁棒性的具体建议，旨在为未来医疗AI系统的公平性发展提供实践指导。
原文摘要 · Abstract (English)
Artificial intelligence (AI) holds great promise for transforming healthcare. However, despite significant advances, the integration of AI solutions into real-world clinical practice remains limited. A major barrier is the quality and fairness of training data, which is often compromised by biased data collection practices. This paper draws on insights from the AI4HealthyAging project, part of Spain's national R&D initiative, where our task was to detect biases during clinical data collection. We identify several types of bias across multiple use cases, including historical, representation, and measurement biases. These biases manifest in variables such as sex, gender, age, habitat, socioeconomic status, equipment, and labeling. We conclude with practical recommendations for improving the fairness and robustness of clinical problem design and data collection. We hope that our findings and experience contribute to guiding future projects in the development of fairer AI systems in healthcare.
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