ReviewPLOS digital health2024
Unmasking biases and navigating pitfalls in the ophthalmic artificial intelligence lifecycle: A narrative review.
Review in PLOS digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Artificial intelligence in cataract management: current status and future perspectives.International ophthalmology · 2026Review
- Artificial intelligence in ophthalmology: From diagnostic accuracy to clinical application.World journal of methodology · 2026Review
- A decade of artificial intelligence research in ophthalmology: Global trends and transferable insights for medical AI.PLOS digital health · 2026Article
- Critical appraisal of fairness metrics for artificial intelligence-based clinical prediction models: a scoping review.The Lancet. Digital health · 2026Article
- Comparing Interactive Machine Learning and Unassisted Segmentation of Geographic Atrophy From Fundus Autofluorescence.Translational vision science & technology · 2026Article
- Artificial intelligence, data sharing, and privacy for retinal imaging under Brazilian Data Protection Law.International journal of retina and vitreous · 2025Article
- A portable retina fundus photos dataset for clinical, demographic, and diabetic retinopathy prediction.Scientific data · 2025Article
- BRSET: A Brazilian Multilabel Ophthalmological Dataset of Retina Fundus Photos.PLOS digital health · 2024Article
- BRSET: A Brazilian Multilabel Ophthalmological Dataset of Retina Fundus Photos.medRxiv : the preprint server for health sciences · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Over the past 2 decades, exponential growth in data availability, computational power, and newly available modeling techniques has led to an expansion in interest, investment, and research in Artificial Intelligence (AI) applications. Ophthalmology is one of many fields that seek to benefit from AI given the advent of telemedicine screening programs and the use of ancillary imaging. However, before AI can be widely deployed, further work must be done to avoid the pitfalls within the AI lifecycle. This review article breaks down the AI lifecycle into seven steps-data collection; defining the model task; data preprocessing and labeling; model development; model evaluation and validation; deployment; and finally, post-deployment evaluation, monitoring, and system recalibration-and delves into the risks for harm at each step and strategies for mitigating them.
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.