ArticleRadiology. Artificial intelligence2023
Hurdles to Artificial Intelligence Deployment: Noise in Schemas and "Gold" Labels.
Article in Radiology. Artificial intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Toward universal representations of the living: Physiological invariance for transportable medical AI.PLOS digital health · 2026Review
- CXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray.Medical image analysis · 2025Article
- Article
- Deep learning analysis for rheumatologic imaging: current trends, future directions, and the role of human.Journal of rheumatic diseases · 2025Review
- Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge.Medical image analysis · 2024Article
- Performance changes due to differences among annotating radiologists for training data in computerized lesion detection.International journal of computer assisted radiology and surgery · 2024Article
- Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge.ArXiv · 2024Article
- Deep learning in rheumatological image interpretation.Nature reviews. Rheumatology · 2024Review
- Empirical data drift detection experiments on real-world medical imaging data.Nature communications · 2024Article
- Early experiences of integrating an artificial intelligence-based diagnostic decision support system into radiology settings: a qualitative study.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- The Subgroup Imperative: Chest Radiograph Classifier Generalization Gaps in Patient, Setting, and Pathology Subgroups.Radiology. Artificial intelligence · 2023Article
- Detecting Common Sources of AI Bias: Questions to Ask When Procuring an AI Solution.Radiology · 2023Article
- The Steep Road to Artificial Intelligence-mediated Radiology.Radiology. Artificial intelligence · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed 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:
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.