研究指出，数据来源与收集环节的偏见，以及算法设计与决策中的偏见，确实可能对边缘群体造成不公平的健康结果。[2]值得注意的是，这种偏见并非偶发的技术故障，而是结构性不平等在数字系统中的延续，它系统性地伤害那些在数据中本就被忽视的人，如种族偏见中的弱势族裔，疾病谱系中的非常见病患者，以及因症状信息复杂而难以转化为标准数据的疾病群体。

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