Fairboard: 一种用于评估医疗模型公平性的量化框架
Fairboard: a quantitative framework for equity assessment of healthcare models
AI总结：
本文评估了18种开源脑肿瘤分割模型在648名胶质瘤患者中的公平性，发现患者身份比模型选择更能解释性能差异，提出Fairboard工具以提升医疗影像模型的公平性监控。
摘要：
尽管已有超过1000种FDA认证的AI医疗设备，但正式的公平性评估仍很少。本文评估了18种开源脑肿瘤分割模型在两个独立数据集中的公平性，发现患者身份比模型选择更能解释性能差异，临床因素如分子诊断、肿瘤分级和切除范围比模型架构更能预测分割准确性。 voxel级空间元分析识别出神经解剖学局部偏见，且在不同模型间具有一致性。在高维潜在空间中，模型性能显著聚类，表明患者特征空间包含算法脆弱性轴。尽管新模型更公平，但无正式公平保证。最后，我们发布Fairboard，一个开源无代码仪表板，降低医疗影像模型公平性监控的障碍。
英文摘要：
Despite there now being more than 1,000 FDA-authorised AI medical devices, formal equity assessments -- whether model performance is uniform across patient subgroups -- are rare. Here, we evaluate the equity of 18 open-source brain tumour segmentation models across 648 glioma patients from two independent datasets (n = 11,664 model inferences) along distinct univariate, Bayesian multivariate, spatial, and representational dimensions. We find that patient identity consistently explains more performance variance than model choice, with clinical factors, including molecular diagnosis, tumour grade, and extent of resection, predicting segmentation accuracy more strongly than model architecture. A voxel-wise spatial meta-analysis identifies neuroanatomically localised biases that are compartment-specific yet often consistent across models. Within a high-dimensional latent space of lesion masks and clinic-demographic features, model performance clusters significantly, indicating that the patient feature space contains axes of algorithmic vulnerability. Although newer models tend toward greater equity, none provide a formal fairness guarantee. Lastly, we release Fairboard, an open-source, no-code dashboard that lowers barriers to equitable model monitoring in medical imaging.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
