The Challenge Dataset – simple evaluation for safe, transparent healthcare AI deployment
Bibliographic record
Abstract
Abstract In this paper, we demonstrate the use of a “Challenge Dataset”: a small, site-specific, manually curated dataset – enriched with uncommon, risk-exposing, and clinically important edge cases – that can facilitate pre-deployment evaluation and identification of clinically relevant AI performance deficits. The five major steps of the Challenge Dataset process are described in detail, including defining use cases, edge case selection, dataset size determination, dataset compilation, and model evaluation. Evaluating performance of four chest X-ray classifiers (one third-party developer model and three models trained on open-source datasets) on a small, manually curated dataset (410 images), we observe a generalization gap of 20.7% (13.5% - 29.1%) for sensitivity and 10.5% (4.3% - 18.3%) for specificity compared to developer-reported values. Performance decreases further when evaluated against edge cases (critical findings: 43.4% [27.4% - 59.8%]; unusual findings: 45.9% [23.1% - 68.7%]; solitary findings 45.9% [23.1% - 68.7%]). Expert manual audit revealed examples of critical model failure (e.g., missed pneumomediastinum) with potential for patient harm. As a measure of effort, we find that the minimum required number of Challenge Dataset cases is about 1% of the annual total for our site (approximately 400 of 40,000). Overall, we find that the Challenge Dataset process provides a method for local pre-deployment evaluation of medical imaging AI models, allowing imaging providers to identify both deficits in model generalizability and specific points of failure prior to clinical deployment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".