AI生成的数据污染侵蚀病理变异性与诊断可靠性
AI-generated data contamination erodes pathological variability and diagnostic reliability
AI总结：
AI生成的数据污染导致病理变异性下降和诊断可靠性降低，需政策强制的人类监督以防止医疗数据生态系统的退化。
摘要：
生成式人工智能（AI）正迅速向医疗记录中填充合成内容，形成一个反馈循环，使得未来模型越来越有风险在未经过审校的AI生成数据上进行训练。然而，这种AI生成数据污染的临床后果仍未被探索。在此，我们证明，在没有强制性的人类验证的情况下，这种自我参照循环导致病理变异性迅速下降和诊断可靠性下降。通过分析超过800,000个合成数据点，涵盖临床文本生成、视觉语言报告和医学图像合成，我们发现，无论模型架构如何，模型都会逐渐向通用表型收敛。具体而言，罕见但关键的发现，包括气胸和积液，从AI模型生成的合成内容中消失，同时人口特征严重偏向中年男性表型。关键的是，这种退化被假诊断信心所掩盖；模型继续发布令人放心的报告，同时未能检测到危及生命的病理，假 reassure 率增加到40%。盲审医生评估确认，这种信心与准确性脱节使AI生成的文档在仅两代后就变得临床无用。我们系统评估了三种缓解策略，发现尽管合成数据量扩大未能防止崩溃，但将真实数据与质量意识过滤混合可有效保持多样性。最终，我们的结果表明，没有政策强制的人类监督，生成式AI的部署可能会破坏其依赖的医疗数据生态系统。
英文摘要：
Generative artificial intelligence (AI) is rapidly populating medical records with synthetic content, creating a feedback loop where future models are increasingly at risk of training on uncurated AI-generated data. However, the clinical consequences of this AI-generated data contamination remain unexplored. Here, we show that in the absence of mandatory human verification, this self-referential cycle drives a rapid erosion of pathological variability and diagnostic reliability. By analysing more than 800,000 synthetic data points across clinical text generation, vision-language reporting, and medical image synthesis, we find that models progressively converge toward generic phenotypes regardless of the model architecture. Specifically, rare but critical findings, including pneumothorax and effusions, vanish from the synthetic content generated by AI models, while demographic representations skew heavily toward middle-aged male phenotypes. Crucially, this degradation is masked by false diagnostic confidence; models continue to issue reassuring reports while failing to detect life-threatening pathology, with false reassurance rates tripling to 40%. Blinded physician evaluation confirms that this decoupling of confidence and accuracy renders AI-generated documentation clinically useless after just two generations. We systematically evaluate three mitigation strategies, finding that while synthetic volume scaling fails to prevent collapse, mixing real data with quality-aware filtering effectively preserves diversity. Ultimately, our results suggest that without policy-mandated human oversight, the deployment of generative AI threatens to degrade the very healthcare data ecosystems it relies upon.
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