ReviewCurrent opinion in ophthalmology2021
Applications of interpretability in deep learning models for ophthalmology.
Review in Current opinion in ophthalmology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Developing and evaluating automated deep learning and human-in-the-loop vision-language systems for microplastic characterization.Scientific reports · 2026Article
- Artificial intelligence for myopia: Current update and concern.Indian journal of ophthalmology · 2026Article
- Current status and solutions for AI ethics in ophthalmology: a bibliometric analysis.NPJ digital medicine · 2025Article
- Diagnostic challenges in high myopia: identification of sight-threatening complications and the role of artificial intelligence.Frontiers in ophthalmology · 2025Review
- Artificial intelligence in ophthalmology: opportunities, challenges, and ethical considerations.Medical hypothesis, discovery & innovation ophthalmology journal · 2025Review
- Using Deep Learning to Distinguish Highly Malignant Uveal Melanoma from Benign Choroidal Nevi.Journal of clinical medicine · 2024Article
- Human-Centered Design to Address Biases in Artificial Intelligence.Journal of medical Internet research · 2023Article
- International publication trends in the application of artificial intelligence in ophthalmology research: an updated bibliometric analysis.Annals of translational medicine · 2023Review
- Automated analysis of vessel morphometry in retinal images from a Danish high street optician setting.PloS one · 2023Article
- A Prediction Nomogram for Recurrent Retinal Detachment.Risk management and healthcare policy · 2023Article
- Deep learning for ultra-widefield imaging: a scoping review.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2022Article
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
Abstract
purpose of reviewIn this article, we introduce the concept of model interpretability, review its applications in deep learning models for clinical ophthalmology, and discuss its role in the integration of artificial intelligence in healthcare. RECENT
findingsThe advent of deep learning in medicine has introduced models with remarkable accuracy. However, the inherent complexity of these models undermines its users' ability to understand, debug and ultimately trust them in clinical practice. Novel methods are being increasingly explored to improve models' 'interpretability' and draw clearer associations between their outputs and features in the input dataset. In the field of ophthalmology, interpretability methods have enabled users to make informed adjustments, identify clinically relevant imaging patterns, and predict outcomes in deep learning models. SUMMARY: Interpretability methods support the transparency necessary to implement, operate and modify complex deep learning models. These benefits are becoming increasingly demonstrated in models for clinical ophthalmology. As quality standards for deep learning models used in healthcare continue to evolve, interpretability methods may prove influential in their path to regulatory approval and acceptance in clinical practice.
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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.