ArticleClinical ophthalmology (Auckland, N.Z.)2023
Artificial Intelligence Improves Patient Follow-Up in a Diabetic Retinopathy Screening Program.
Article in Clinical ophthalmology (Auckland, N.Z.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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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.
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Who cites it
15 citing papers in PubMed, 20 citations in OpenAlex.
- Accuracy and Effectiveness of AI-Powered Systems in Patient Counseling, Education, and Management in Optometry and Related Eye-Care Settings: A Systematic Review.Healthcare (Basel, Switzerland) · 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
- Financial incentives increase uptake and perceived effectiveness of autonomous medical AI, yet patients still seek human reconfirmation.NPJ digital medicine · 2026Article
- Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis.NPJ digital medicine · 2026Article
- Autonomous Artificial Intelligence in Diabetic Retinopathy Testing-Lessons Learned on Successful Health System Adoption.Ophthalmology science · 2026Article
- The AI-Augmented Ophthalmologist for chronic ocular diseases: a patient-centered framework for human-AI collaboration.Frontiers in cell and developmental biology · 2026Review
- The Role of Artificial Intelligence in the Diagnosis and Management of Diabetic Retinopathy.Journal of clinical medicine · 2025Review
- Application of Artificial Intelligence to Deliver Healthcare From the Eye.JAMA ophthalmology · 2025Review
- A scoping review of artificial intelligence as a medical device for ophthalmic image analysis in Europe, Australia and America.NPJ digital medicine · 2025Article
- Artificial Intelligence Improves Patient Follow-Up in a Diabetic Retinopathy Screening Program [Letter].Clinical ophthalmology (Auckland, N.Z.) · 2025Article
- Valuable insights into general practice staff's experiences and perspectives on AI-assisted diabetic retinopathy screening-An interview study.Frontiers in medicine · 2025Article
- Detection Rate of Diabetic Retinopathy Before and After Implementation of Autonomous AI-based Fundus Photograph Analysis in a Resource-Limited Area in Belize.Clinical ophthalmology (Auckland, N.Z.) · 2025Article
- Mitigation of AI adoption bias through an improved autonomous AI system for diabetic retinal disease.NPJ digital medicine · 2024Article
- Implementation of Artificial Intelligence-Based Diabetic Retinopathy Screening in a Tertiary Care Hospital in Quebec: Prospective Validation Study.JMIR diabetes · 2024Article
- Modern Approach to Diabetic Retinopathy Diagnostics.Diagnostics (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
Purpose: We examine the rate of and reasons for follow-up in an Artificial Intelligence (AI)-based workflow for diabetic retinopathy (DR) screening relative to two human-based workflows. Patients and Methods: A DR screening program initiated September 2019 between one institution and its affiliated primary care and endocrinology clinics screened 2243 adult patients with type 1 or 2 diabetes without a diagnosis of DR in the previous year in the San Francisco Bay Area. For patients who screened positive for more-than-mild-DR (MTMDR), rates of follow-up were calculated under a store-and-forward human-based DR workflow ("Human Workflow"), an AI-based workflow involving IDx-DR ("AI Workflow"), and a two-step hybrid workflow ("AI-Human Hybrid Workflow"). The AI Workflow provided results within 48 hours, whereas the other workflows took up to 7 days. Patients were surveyed by phone about follow-up decisions. Results: Under the AI Workflow, 279 patients screened positive for MTMDR. Of these, 69.2% followed up with an ophthalmologist within 90 days. Altogether 70.5% ( Conclusion: Ophthalmology follow-up after a positive DR screening result is approximately three-fold higher under the AI Workflow than either the Human Workflow or AI-Human Hybrid Workflow. Improved follow-up behavior may be due to the decreased time to screening result.
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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.