ArticleNPJ digital medicine2024
Mitigation of AI adoption bias through an improved autonomous AI system for diabetic retinal disease.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05808699 (Camera Qualification of an Additional Fundus Camera Paired With An Autonomous AI for Detecting Diabetic Retinopathy), which is not on this map. Cited by 8 papers.
What it found
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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.
Camera Qualification of an Additional Fundus Camera Paired With An Autonomous AI for Detecting Diabetic Retinopathy
Who cites it
8 citing papers in PubMed.
- Autonomous artificial intelligence prescribing a drug to prevent severe acute graft-versus-host disease in HLA-haploidentical transplants.Nature communications · 2025Trial
- Evaluating Artificial Intelligence as a First-Pass Reader in Fundus Photograph Screening: A Multireader Workflow Validation Study.Diagnostics (Basel, Switzerland) · 2026Article
- The role of agentic artificial intelligence in healthcare: a scoping review.NPJ digital medicine · 2026Article
- Autonomous AI-driven point-of-care screening for diabetic retinopathy compared to reading center multi-expert clinical review: results from three prospective controlled pivotal validation studies with AEYE-DS in over 1,200 patients.Frontiers in digital health · 2026Article
- Evaluating commercial multimodal AI for diabetic eye screening and implications for an alternative regulatory pathway.NPJ digital medicine · 2025Article
- Code-Free Machine Learning for the Detection of Common Ophthalmic Diseases.Translational vision science & technology · 2025Article
- Factors Affecting the Receptiveness of Chinese Internists and Surgeons Toward Artificial Intelligence-Driven Drug Prescription: Protocol for a Systematic Survey Study.JMIR research protocols · 2025Article
- Ethical and legal considerations in healthcare AI: innovation and policy for safe and fair use.Royal Society open science · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Where adopted, Autonomous artificial Intelligence (AI) for Diabetic Retinal Disease (DRD) resolves longstanding racial, ethnic, and socioeconomic disparities, but AI adoption bias persists. This preregistered trial determined sensitivity and specificity of a previously FDA authorized AI, improved to compensate for lower contrast and smaller imaged area of a widely adopted, lower cost, handheld fundus camera (RetinaVue700, Baxter Healthcare, Deerfield, IL) to identify DRD in participants with diabetes without known DRD, in primary care. In 626 participants (1252 eyes) 50.8% male, 45.7% Hispanic, 17.3% Black, DRD prevalence was 29.0%, all prespecified non-inferiority endpoints were met and no racial, ethnic or sex bias was identified, against a Wisconsin Reading Center level I prognostic standard using widefield stereoscopic photography and macular Optical Coherence Tomography. Results suggest this improved autonomous AI system can mitigate AI adoption bias, while preserving safety and efficacy, potentially contributing to rapid scaling of health access equity. ClinicalTrials.gov NCT05808699 (3/29/2023).
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.