AI-generated data contamination erodes pathological variability and diagnostic reliability
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2026
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| author | He, Hongyu Xiang, Shaowen Zhang, Ye Zhu, Yingtao Zhang, Jin Deng, Hao Alsentzer, Emily Liu, Yun Chen, Qingyu Yu, Kun-Hsing Marshall, Andrew Chen, Tingting Anumasa, Srinivas Ebner, Daniel Ho, Dean Ngiam, Kee Yuan Cheng, Ching-Yu Liu, Dianbo |
| author_facet | He, Hongyu Xiang, Shaowen Zhang, Ye Zhu, Yingtao Zhang, Jin Deng, Hao Alsentzer, Emily Liu, Yun Chen, Qingyu Yu, Kun-Hsing Marshall, Andrew Chen, Tingting Anumasa, Srinivas Ebner, Daniel Ho, Dean Ngiam, Kee Yuan Cheng, Ching-Yu Liu, Dianbo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_12946 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AI-generated data contamination erodes pathological variability and diagnostic reliability He, Hongyu Xiang, Shaowen Zhang, Ye Zhu, Yingtao Zhang, Jin Deng, Hao Alsentzer, Emily Liu, Yun Chen, Qingyu Yu, Kun-Hsing Marshall, Andrew Chen, Tingting Anumasa, Srinivas Ebner, Daniel Ho, Dean Ngiam, Kee Yuan Cheng, Ching-Yu Liu, Dianbo Computers and Society Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Machine Learning 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. |
| title | AI-generated data contamination erodes pathological variability and diagnostic reliability |
| topic | Computers and Society Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2601.12946 |