Hurdles to Artificial Intelligence Deployment: Noise in Schemas and “Gold” Labels
Bibliographic record
Abstract
Despite frequent reports of imaging artificial intelligence (AI) that parallels human performance, clinicians often question the safety and robustness of AI products in practice. This work explores two underreported sources of noise that negatively affect imaging AI: (a) variation in labeling schema definitions and (b) noise in the labeling process. First, the overlap between the schemas of two publicly available datasets and a third-party vendor are compared, showing there is low agreement (<50%) between them. The authors also highlight the problem of label inconsistency, where different annotation schemas are selected for the same clinical prediction task; this results in inconsistent use of medical ontologies through intermingling or duplicate observations and diseases. Second, the individual radiologist annotations for the CheXpert test set are used to quantify noise in the labeling process. The analysis demonstrated that label noise varies by class, as agreement was high for pneumothorax and medical devices (percent agreement > 90%). Among low agreement classes (pneumonia, consolidation), the labels assigned as “ground truth” were unreliable, suggesting that the result of majority voting is highly dependent on which group of radiologists is assigned to annotation. Noise in labeling schemas and gold label annotations are pervasive in medical imaging classification and affect downstream clinical deployment. Possible solutions (eg, changes to task design, annotation methods, and model training) and their potential to improve trust in clinical AI are discussed. Keywords: Radiology AI, Dataset Creation, Noise in Datasets Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Ursprung and Woitek in this issue.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.136 | 0.360 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".