ArticleFrontiers in artificial intelligence2025
Assessment of demographic bias in retinal age prediction machine learning models.
Article in Frontiers in artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Human-AI Systems in Medicine: Outskilling Versus Newskilling.Annals of biomedical engineering · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The retinal age gap, defined as the difference between the predicted retinal age and chronological age, is an emerging biomarker for many eye conditions and even non-ocular diseases. Machine learning (ML) models are commonly used for retinal age prediction. However, biases in ML models may lead to unfair predictions for some demographic groups, potentially exacerbating health disparities. This retrospective cross-sectional study evaluated demographic biases related to sex and ethnicity in retinal age prediction models using retinal imaging data (color fundus photography [CFP], optical coherence tomography [OCT], and combined CFP + OCT) from 9,668 healthy individuals (mean age 56.8 years; 52% female) in the UK Biobank. The RETFound foundation model was fine-tuned to predict retinal age, and bias was assessed by comparing mean absolute error (MAE) and retinal age gaps across demographic groups. The combined CFP + OCT model achieved the lowest MAE (3.01 years), outperforming CFP-only (3.40 years) and OCT-only (4.37 years) models. Significant sex differences were observed only in the CFP model (
Indexed as
Identifiers
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.