| Title: | Data-hugging shields proprietary AI models from research that could disprove them |
| Journal: | npj Artificial Intelligence |
| Published: | 17 Apr 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42005533/ |
| DOI: | https://doi.org/10.1038/s44387-026-00094-2 |
| URL: | https://www.nature.com/articles/s44387-026-00094-2.pdf |
Publication 17959
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Abstract
Abstract"Data hugging" blocks independent verification of medical AI. Apple claims age estimation with a mean absolute error of 2.9 years using photoplethysmographic (PPG) signals. Given PPG's noise, such accuracy is questionable, raising concerns about other tech companies' claims. Using UK Biobank data, we find this accuracy unreplicable, achieving results only marginally better than predicting mean age. We advocate for curated public benchmark datasets and evaluation platforms to protect the public from unverifiable claims.</p>
4 Authors
- Anish Karpurapu
- Zhicheng Guo
- Xiao Hu
- Cynthia Rudin
1 Application
| Application ID | Title |
|---|---|
| 549696 | Age estimation and cardiac disease prediction from PPG signals using interpretable machine learning models. |
Enabling scientific discoveries that improve human health