ArticleThe British journal of ophthalmology2019
Universal artificial intelligence platform for collaborative management of cataracts.
Article in The British journal of ophthalmology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 1 of them a synthesis that pooled it.
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
65 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review.Journal of medical Internet research · 2021Pooled it
- The effectiveness of large language models in medical AI research for physicians: A randomized controlled trial.Cell reports. Medicine · 2025Trial
- AI for Anterior Segment Disease Using Transfer Learning: Adapting Slit-Lamp-Trained Model for Analysis of Smartphone Corneal Images.Translational vision science & technology · 2026Article
- Artificial Intelligence in Ophthalmology: Current Status, Challenges, and Future Perspectives.Health care science · 2026Review
- Impact of artificial intelligence on the availability, accessibility, acceptability and quality of ophthalmic disease screening services: a scoping review.The British journal of ophthalmology · 2026Article
- The aging eye: navigating molecular mechanisms and innovative interventions.Biogerontology · 2026Review
- Prediction of Cataract Severity Using Slit Lamp Images from a Portable Smartphone Device: A Pilot Study.Sensors (Basel, Switzerland) · 2026Article
- Smartphone-Based Portable Slit Lamp in Anterior Segment Diseases: A Narrative Review of Clinical Assessment and Integration with Artificial Intelligence.Ophthalmology and therapy · 2026Review
- A fine-grained fundus image dataset for cataract severity assessment and diagnosis.Scientific data · 2026Article
- Transfer learning and vision transformer for the automatic diagnosis of cataracts in ophthalmological images.Frontiers in digital health · 2026Article
- A Deep Learning Model for Detecting the Eyes Receiving Glaucoma Medications Using Anterior Segment Images.Translational vision science & technology · 2025Article
- Application of artificial intelligence in myopia prevention and control.Pediatric investigation · 2025Review
- Machine Learning-Based Explainable Automated Nonlinear Computation Scoring System for Health Score and an Application for Prediction of Perioperative Stroke: Retrospective Study.Journal of medical Internet research · 2025Article
- Artificial intelligence for posterior capsule opacification.Frontiers in medicine · 2025Review
- Evaluation of the effectiveness of a portable device for diagnosing cataract.Arquivos brasileiros de oftalmologia · 2025Article
- Ocular surface disease following LASIK and cataract surgery: a review of their interrelated complications.Frontiers in medicine · 2025Review
- A deep learning-driven cataract screening model derived from multicenter real-world dataset.Frontiers in medicine · 2025Article
- Sustainability in Cataract and Refractive Surgery: Current Challenges and Future Perspectives.Journal of ophthalmology · 2025Review
- Artificial intelligence in cataract grading system: a LOCS III-based hybrid model achieving high-precision classification.Frontiers in cell and developmental biology · 2025Article
- Tools and methods for cataract recognition in low-resource settings: A narrative review.PLOS global public health · 2025Review
5 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
37 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo establish and validate a universal artificial intelligence (AI) platform for collaborative management of cataracts involving multilevel clinical scenarios and explored an AI-based medical referral pattern to improve collaborative efficiency and resource coverage.
methodsThe training and validation datasets were derived from the Chinese Medical Alliance for Artificial Intelligence, covering multilevel healthcare facilities and capture modes. The datasets were labelled using a three-step strategy: (1) capture mode recognition; (2) cataract diagnosis as a normal lens, cataract or a postoperative eye and (3) detection of referable cataracts with respect to aetiology and severity. Moreover, we integrated the cataract AI agent with a real-world multilevel referral pattern involving self-monitoring at home, primary healthcare and specialised hospital services.
resultsThe universal AI platform and multilevel collaborative pattern showed robust diagnostic performance in three-step tasks: (1) capture mode recognition (area under the curve (AUC) 99.28%-99.71%), (2) cataract diagnosis (normal lens, cataract or postoperative eye with AUCs of 99.82%, 99.96% and 99.93% for mydriatic-slit lamp mode and AUCs >99% for other capture modes) and (3) detection of referable cataracts (AUCs >91% in all tests). In the real-world tertiary referral pattern, the agent suggested 30.3% of people be 'referred', substantially increasing the ophthalmologist-to-population service ratio by 10.2-fold compared with the traditional pattern.
conclusionsThe universal AI platform and multilevel collaborative pattern showed robust diagnostic performance and effective service for cataracts. The context of our AI-based medical referral pattern will be extended to other common disease conditions and resource-intensive situations.
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.