Recent research shows that medical AI models are vulnerable to membership inference attacks (MIAs), which can reveal private patient information. This risk is not the same for everyone, as people from underrepresented groups are more likely to have their identities exposed.

最近的研究顯示，醫療 AI 模型容易受到成員推理攻擊（MIAs）的影響，可能會洩漏病患的隱私資訊。這種風險對每個人並不相同，因為代表性不足的群體更有可能被揭露身份。

The problem begins with the ability of MIAs to determine if a specific patient's data was used to train an AI model. While most reports focus on average success rates, this study emphasizes that such numbers hide the high risks faced by individual patients. By testing seven different clinical datasets, including medical images and health records, researchers found that some patients were almost certain to be identified.

問題在於 MIAs 能夠判定特定病患的數據是否被用於訓練 AI 模型。雖然大多數報告集中在平均成功率，但本研究強調，此類數據掩蓋了個別病患所面臨的高風險。研究人員透過測試七個不同的臨床數據集（包括醫療影像與健康記錄），發現某些病患幾乎肯定會被識別出來。

Furthermore, the study suggests that as AI models become more complex, such as when using vision transformers, the number of vulnerable patients increases significantly. This creates a conflict between wanting the most accurate diagnosis and protecting patient privacy, especially for those with rare medical conditions.

此外，研究指出，隨著 AI 模型變得更加複雜（例如使用視覺轉換器時），易受攻擊的病患數量會顯著增加。這在追求最準確診斷與保護病患隱私之間製造了衝突，對於患有罕見疾病的人來說尤其如此。

Finally, the research highlights a systemic inequality in these risks. Patients from minority groups, based on race, insurance status, or rare diseases, face a much higher risk of data exposure. This happens because the AI must work harder to fit these unusual data points to perform well. Consequently, using these models without strong protections could increase health inequalities by putting marginalized populations at greater risk.

最後，研究強調了這些風險中的系統性不平等。基於種族、保險狀態或罕見疾病的少數群體病患，面臨數據洩漏的風險高得多。這是因為 AI 必須更努力地擬合這些不尋常的數據點以提升表現。因此，在缺乏強效保護的情況下使用這些模型，可能會讓邊緣化群體面臨更高風險，進而加劇健康不平等。

The study concludes that reporting average privacy levels is not enough. It recommends using patient-level differential privacy (DP) and strict access controls to prevent the theft of sensitive data.

研究結論認為，僅報告平均隱私水平是不夠的。研究建議使用病患級別的差分隱私（DP）和嚴格的訪問控制，以防止敏感數據被盜用。