Concerns Raised Over Unverified Datasets in AI Health Prediction Models

A new study finds widely used AI health prediction models are built on datasets with unverifiable origins, raising safety and validity concerns.
Key Details
- 1Researchers at QUT and AusHSI examined two popular health datasets on Kaggle used for stroke and diabetes prediction.
- 2These datasets have been cited in 125 peer-reviewed studies, with little to no information about data provenance.
- 3Three AI models trained on these datasets have been used in clinical practice; one was cited in a medical device patent.
- 4The datasets scored 0/9 on the TRIPOD+AI data provenance criteria, indicating they are unsuitable for clinical use.
- 5Seven articles based on these datasets have already been retracted as unreliable.
- 6Researchers urge journals, funders, and data repositories to enforce stricter data-source disclosure, and recommend removal of the problematic datasets.
Why It Matters

Source
EurekAlert
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