Revolutionizing Diabetic Retinopathy Screening: Integrating AI-Based Retinal Imaging in Primary Care
Diabetic retinopathy (DR) is a public health issue affecting millions in the United States and Europe. However, despite strong recommendations for screening at regular intervals by many professional societies, including the American Diabetes Association and the American Academy of Ophthalmology, scr...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Journal of CME |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/28338073.2024.2437294 |
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| author | Dean Beals Lesley Simon Faith Rogers Stan Pogroszewski |
| author_facet | Dean Beals Lesley Simon Faith Rogers Stan Pogroszewski |
| author_sort | Dean Beals |
| collection | DOAJ |
| description | Diabetic retinopathy (DR) is a public health issue affecting millions in the United States and Europe. However, despite strong recommendations for screening at regular intervals by many professional societies, including the American Diabetes Association and the American Academy of Ophthalmology, screening rates remain suboptimal, with only 50–70% of patients with diabetes adhering to recommended annual eye exams. Barriers to screening include lack of awareness, socioeconomic factors, health care system fragmentation, and workforce shortages, among others. Artificial intelligence (AI)-based retinal screening tools offer promising solutions to improve DR detection in primary care settings. We describe a quality improvement and continuing medical education programme, starting in 2020, which has so far deployed 198 AI-equipped cameras in 5 health systems, covering approximately 151,000 patients with diabetes. To date, over 20,000 screenings were completed, with more than mild DR detected in more than 3,450 people, leading to specialist referrals for follow-up care. Notably, negative screenings potentially represent deferred specialist care. While AI adoption in healthcare presents challenges, its potential benefits in improving patient care and optimising resources are significant. Integrating AI-based DR screening with a comprehensive education and process improvement initiative in primary care practices warrants serious consideration, promising to enhance patient outcomes. |
| format | Article |
| id | doaj-art-eae352f2b5cd490594b5babd50e3f975 |
| institution | Kabale University |
| issn | 2833-8073 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of CME |
| spelling | doaj-art-eae352f2b5cd490594b5babd50e3f9752025-01-21T15:13:18ZengTaylor & Francis GroupJournal of CME2833-80732025-12-0114110.1080/28338073.2024.2437294Revolutionizing Diabetic Retinopathy Screening: Integrating AI-Based Retinal Imaging in Primary CareDean Beals0Lesley SimonFaith RogersStan PogroszewskiDKBmed, New York, NY, USADiabetic retinopathy (DR) is a public health issue affecting millions in the United States and Europe. However, despite strong recommendations for screening at regular intervals by many professional societies, including the American Diabetes Association and the American Academy of Ophthalmology, screening rates remain suboptimal, with only 50–70% of patients with diabetes adhering to recommended annual eye exams. Barriers to screening include lack of awareness, socioeconomic factors, health care system fragmentation, and workforce shortages, among others. Artificial intelligence (AI)-based retinal screening tools offer promising solutions to improve DR detection in primary care settings. We describe a quality improvement and continuing medical education programme, starting in 2020, which has so far deployed 198 AI-equipped cameras in 5 health systems, covering approximately 151,000 patients with diabetes. To date, over 20,000 screenings were completed, with more than mild DR detected in more than 3,450 people, leading to specialist referrals for follow-up care. Notably, negative screenings potentially represent deferred specialist care. While AI adoption in healthcare presents challenges, its potential benefits in improving patient care and optimising resources are significant. Integrating AI-based DR screening with a comprehensive education and process improvement initiative in primary care practices warrants serious consideration, promising to enhance patient outcomes.https://www.tandfonline.com/doi/10.1080/28338073.2024.2437294Diabetic retinopathy screeningDR, artificial intelligence in healthcareprimary care AI integrationAI-based retinal imaging |
| spellingShingle | Dean Beals Lesley Simon Faith Rogers Stan Pogroszewski Revolutionizing Diabetic Retinopathy Screening: Integrating AI-Based Retinal Imaging in Primary Care Journal of CME Diabetic retinopathy screening DR, artificial intelligence in healthcare primary care AI integration AI-based retinal imaging |
| title | Revolutionizing Diabetic Retinopathy Screening: Integrating AI-Based Retinal Imaging in Primary Care |
| title_full | Revolutionizing Diabetic Retinopathy Screening: Integrating AI-Based Retinal Imaging in Primary Care |
| title_fullStr | Revolutionizing Diabetic Retinopathy Screening: Integrating AI-Based Retinal Imaging in Primary Care |
| title_full_unstemmed | Revolutionizing Diabetic Retinopathy Screening: Integrating AI-Based Retinal Imaging in Primary Care |
| title_short | Revolutionizing Diabetic Retinopathy Screening: Integrating AI-Based Retinal Imaging in Primary Care |
| title_sort | revolutionizing diabetic retinopathy screening integrating ai based retinal imaging in primary care |
| topic | Diabetic retinopathy screening DR, artificial intelligence in healthcare primary care AI integration AI-based retinal imaging |
| url | https://www.tandfonline.com/doi/10.1080/28338073.2024.2437294 |
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