ReviewStatistical methods in medical research2023
Role of calibration in uncertainty-based referral for deep learning.
Review in Statistical methods in medical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed, 7 citations in OpenAlex.
- Uncertainty quantification for artificial intelligence in medical imaging: what every radiologist needs to know.Abdominal radiology (New York) · 2026Review
- Confidence-calibrated federated graph attention for internet of things agents under latency SLOs.Scientific reports · 2026Article
- A novel deep learning approach for mosquito species classification via a dual-head structure and calibration-aware fusion architecture.Scientific reports · 2026Article
- Artificial Intelligence Applications in the Treatment of Colorectal Cancer: A Narrative Review.Clinical Medicine Insights. Oncology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
The uncertainty in predictions from deep neural network analysis of medical imaging is challenging to assess but potentially important to include in subsequent decision-making. Using data from diabetic retinopathy detection, we present an empirical evaluation of the role of model calibration in uncertainty-based referral, an approach that prioritizes referral of observations based on the magnitude of a measure of uncertainty. We consider several configurations of network architecture, methods for uncertainty estimation, and training data size. We identify a strong relationship between the effectiveness of uncertainty-based referral and having a well-calibrated model. This is especially relevant as complex deep neural networks tend to have high calibration errors. Finally, we show that post-calibration of the neural network helps uncertainty-based referral with identifying hard-to-classify observations.
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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.