医学AI过度关注平均病例，忽视罕见但关键的重症，导致误诊漏诊。
The Average Patient Fallacy
- 用临床加权目标优化模型，提升对罕见病例的检测能力。
- 在肿瘤、心血管和眼科案例中，发现罕见响应者被大量漏诊。
- 提出可量化的罕见病例评估指标，帮助医生做伦理决策。
医学机器学习通常以群体平均为优化目标，这种频率加权训练偏向常见表现，弱化了罕见但临床关键的病例，我们称之为‘平均患者谬误’。在混合模型中，稀有病例的梯度因出现频率低而被抑制，与精准医疗的目标直接冲突。肿瘤学、心脏病学和眼科学中的临床案例显示，这导致罕见有效响应者被遗漏、非典型急症识别延迟，以及对致盲性变异表现不佳。我们提出若干可操作改进：罕见病例性能差距、罕见病例校准误差、基于流行率的罕见性定义，以及体现临床伦理优先级的加权目标。权重选择应经过结构化讨论。医学AI必须识别异常病例，因其具有重大临床意义。
原文摘要 · Abstract (English)
Machine learning in medicine is typically optimized for population averages. This frequency weighted training privileges common presentations and marginalizes rare yet clinically critical cases, a bias we call the average patient fallacy. In mixture models, gradients from rare cases are suppressed by prevalence, creating a direct conflict with precision medicine. Clinical vignettes in oncology, cardiology, and ophthalmology show how this yields missed rare responders, delayed recognition of atypical emergencies, and underperformance on vision-threatening variants. We propose operational fixes: Rare Case Performance Gap, Rare Case Calibration Error, a prevalence utility definition of rarity, and clinically weighted objectives that surface ethical priorities. Weight selection should follow structured deliberation. AI in medicine must detect exceptional cases because of their significance.
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