Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy
Open accessAbstract
Deep learning evolves into a new form of machine learning technology that is classified under artificial intelligence (AI), which has substantial potential for large-scale healthcare screening and may allow the determination of the most appropriate specific treatment for individual patients. Recent developments in diagnostic technologies facilitated studies on retinal conditions and ocular disease in metabolism and endocrinology. Globally, diabetic retinopathy (DR) is regarded as a major cause of vision loss. Deep learning systems are effective and accurate in the detection of DR from digital fundus photographs or optical coherence tomography. Thus, using AI techniques, systems with high accuracy and efficiency can be developed for diagnosing and screening DR at an early stage and without the resources that are only accessible in special clinics. Deep learning enables early diagnosis with high specificity and sensitivity, which makes decisions based on minimally handcrafted features paving the way for personalized DR progression real-time monitoring and in-time ophthalmic or endocrine therapies. This review will discuss cutting-edge AI algorithms, the automated detecting systems of DR stage grading and feature segmentation, the prediction of DR outcomes and therapeutics, and the ophthalmic indications of other systemic diseases revealed by AI.
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Amidst rising DR prevalence, autonomous AI testing presents a promising solution to facilitate early detection, promote equitable access, and drive down system-level cost of care.
“Amidst rising DR prevalence, autonomous AI testing presents a promising solution to facilitate early detection, promote equitable access, and drive down system-level cost of care.42,48,67”
From Autonomous Artificial Intelligence in Diabetic Retinopathy Testing—Lessons Learned on Successful Health System Adoption (2026) · cites this paper as [48]
Amidst rising DR prevalence, autonomous AI testing presents a promising solution to facilitate early detection, promote equitable access, and drive down system-level cost of care.
“Amidst rising DR prevalence, autonomous AI testing presents a promising solution to facilitate early detection, promote equitable access, and drive down system-level cost of care.42,48,67”
From Autonomous Artificial Intelligence in Diabetic Retinopathy Testing—Lessons Learned on Successful Health System Adoption (2026) · cites this paper as [67]
Johns Hopkins University found that the percentage of diabetes patients who received annual eye examinations increased from 46.4% in nontesting clinics to 64% in AI-testing clinics in 2021.
“Johns Hopkins University found the percentage of diabetes patients who received annual eye examinations increased from 46.4% in nontesting clinics to 64% in AI-testing clinics in 2021.48”
From Autonomous Artificial Intelligence in Diabetic Retinopathy Testing—Lessons Learned on Successful Health System Adoption (2026) · cites this paper as [48]
The Introduction (page unavailable) discusses AI-based diabetic retinopathy screening generally, but does not report annual eye-examination rates. Searches for 46.4%, 64%, Johns Hopkins University, and clinic-level screening uptake found no passage in our copy addressing the claimed 2021 comparison. (LLM, conf=0.76)
Experimental — accuracy not yet measuredA pivotal retrospective study from 2019 performed by Eyenuk with >100,000 patients tested by the software demonstrated a 91.3% sensitivity and 91.1% specificity.
“A pivotal retrospective study from 2019 performed by Eyenuk with >100 000 patients tested by the software demonstrated a 91.3% sensitivity and 91.1% specificity.”
From Autonomous Artificial Intelligence in Diabetic Retinopathy Testing—Lessons Learned on Successful Health System Adoption (2026) · cites this paper as [34]
The Automated detection and classification section reports performance figures for other diabetic-retinopathy algorithms, including a 91.1% specificity for Ting et al.’s model. Searches of our copy for Eyenuk, EyeArt, the claimed study size, and 91.3% sensitivity found no passage describing the attributed 2019 retrospective study or its results; no page number is available in our copy. (LLM, conf=0.74)
Experimental — accuracy not yet measuredThese findings, along with EyeArt's use in the European Union and Canada, bolstered the credibility that EyeArt could be a clinically utilized AI-guided DR testing program in the United States.
“These findings, along with its use in the European Union and Canada, bolstered the credibility that EyeArt could be a clinically utilized AI-guided DR testing program in the United States.34”
From Autonomous Artificial Intelligence in Diabetic Retinopathy Testing—Lessons Learned on Successful Health System Adoption (2026) · cites this paper as [34]
The Introduction and Conclusion discuss AI-based diabetic-retinopathy screening and commercial systems generally, but do not identify EyeArt. Searches for EyeArt, its use in the European Union and Canada, and its potential clinical use in the United States found no passage addressing those claims. (LLM, conf=0.74)
Experimental — accuracy not yet measured
Combining both AI and human interpretation into a hybrid workflow improved testing sensitivity to 95.5% and specificity to 98.2%.
“Combining both the AI and human interpretation into a hybrid workflow as described earlier, this group found improvement of testing sensitivity to 95.5% and specificity to 98.2%.48”
From Autonomous Artificial Intelligence in Diabetic Retinopathy Testing—Lessons Learned on Successful Health System Adoption (2026) · cites this paper as [48]
The Automated detection and classification section reports performance figures for automated models, but not a hybrid AI–human workflow with 95.5% sensitivity and 98.2% specificity. Searches for both figures and for combined human–AI interpretation found no passage in our copy reporting the claimed result. (LLM, conf=0.72)
Experimental — accuracy not yet measured
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References 90 · Cited by 115
- Authors
- Huang, X., Wang, H., She, C., Feng, J., Liu, X., Hu, X., Chen, L., & Tao, Y.
- Affiliations
- Capital Medical University (CN)
- Corresponding author
- Tao, Yong
- Venue
- Frontiers in Endocrinology
- Publisher
- Frontiers Media SA
- Published
- 2022-09-29
- Volume
- 13
- Pages
- 946915-946915
- ISSN
- 1664-2392 (ISSN-L)
- PMC
- PMC9559815
- OpenAlex
- W4298003910
- Cited by
- 115
- References
- 90
- Citations by year
- 2026: 22 · 2025: 41 · 2024: 36 · 2023: 15 · 2022: 1
- FWCI
- 3.94
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Endocrinology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (32000485, 62006161 , 82070948)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
- Health Sciences › Medicine › Ophthalmology › Ophthalmology and Visual Health Research
- Keywords
- diabetic retinopathy, artificial intelligence, deep learning, automated diagnosis, screening, fundus images, optical coherence tomography, outcome prediction, personalized treatment, vision loss
- MeSH terms
- Machine Learning, Algorithms, Artificial Intelligence, Diabetes Mellitus, Diabetic Retinopathy, Diagnostic Techniques, Ophthalmological, Humans, Retinal Diseases
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