The SEE Study: safety, efficacy, and equity of implementing autonomous artificial intelligence for diagnosing diabetic retinopathy in youth
Open accessAbstract
OBJECTIVE: Diabetic retinopathy (DR) is a leading cause of vision loss worldwide. Screening for DR is recommended in children and adolescents, but adherence is poor. Recently, autonomous artificial intelligence (AI) systems have been developed for early detection of DR and have been included in the American Diabetes Association’s guidelines for screening in adults. We sought to determine the diagnostic efficacy of autonomous AI for the diabetic eye exam in youth with diabetes. RESEARCH DESIGN AND METHODS: In this prospective study, point-of-care diabetic eye exam was implemented using a nonmydriatic fundus camera with an autonomous AI system for detection of DR in a multidisciplinary pediatric diabetes center. Sensitivity, specificity, and diagnosability of AI was compared with consensus grading by retinal specialists, who were masked to AI output. Adherence to screening guidelines was measured before and after AI implementation. RESULTS: Three hundred ten youth with diabetes aged 5–21 years were included, of whom 4.2% had DR. Diagnosability of AI was 97.5% (302 of 310). The sensitivity and specificity of AI to detect more-than-mild DR was 85.7% (95% CI 42.1–99.6%) and 79.3% (74.3–83.8%), respectively, compared with the reference standard as defined by retina specialists. Adherence improved from 49% to 95% after AI implementation. CONCLUSIONS: Use of a nonmydriatic fundus camera with autonomous AI was safe and effective for the diabetic eye exam in youth in our study. Adherence to screening guidelines improved with AI implementation. As the prevalence of diabetes increases in youth and adherence to screening guidelines remains suboptimal, effective strategies for diabetic eye exams in this population are needed.
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In this study, participants' eyes were not pharmacologically dilated, as pilot studies found that pharmacologic dilation is unnecessary in youth.
“In this study, the participant’s eyes were not pharmacologically dilated, as pilot studies found that pharmacologic dilation is unnecessary in youth30.”
From Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial (2024) · cites this paper as [30]
The Research Design and Methods section says, “No pupil dilation was used,” and the paper states that digital fundus photography can be performed without dilation (Abstract; page unavailable). Our copy does not say that pilot studies established pharmacologic dilation was unnecessary in youth, so the stated reason is unsupported. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe effectiveness of autonomous AI to increase adherence and follow-up compared to traditional referral has not been evaluated in a rigorously designed randomized trial.
“the effectiveness of autonomous AI to increase adherence and follow-up compared to traditional referral has not been evaluated in a rigorously designed randomized trial.”
From Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial (2024) · cites this paper as [30]
The Conclusions state that studies assigning patients to AI-based versus traditional screening are needed to determine AI’s effect on adherence. This supports the absence of such a comparison in this pediatric study, but the paper does not establish that no randomized trial exists more broadly or report a trial comparison of follow-up after referral. (LLM, conf=0.83)
Experimental — accuracy not yet measuredIn prior studies of youth with diabetes, the diagnosability of autonomous AI in youth was 97.5%, with 85.7% sensitivity and 79.3% specificity in detecting DED, with no difference in diagnosability across demographic groups.
“In prior studies of youth with diabetes, we demonstrated that the diagnosability of autonomous AI in youth was 97.5%, with 85.7% sensitivity and 79.3% specificity in detecting DED, with no difference in diagnosability across demographic groups30.”
From Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial (2024) · cites this paper as [30]
The Results report 97.5% diagnosability, 85.7% sensitivity, and 79.3% specificity in the youth cohort, with no significant effect of race, ethnicity, age, or sex on diagnosability. The sensitivity and specificity, however, are specifically for detecting more-than-mild diabetic retinopathy, not diabetic eye disease broadly. (LLM, conf=0.84)
Experimental — accuracy not yet measuredIn a previous study, the risk of false negatives for autonomous AI in youth was low.
“though we showed in a previous study that the risk of false negatives is low30.”
From Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial (2024) · cites this paper as [30]
Some participants in this study were familiar with autonomous AI diabetic eye exams from a prior study and may have been more accepting of participation in the current study.
“Some of the participants in this study were familiar with autonomous AI diabetic eye exams from our prior study and may have been more accepting of participation in the current study30.”
From Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial (2024) · cites this paper as [30]
This system can be implemented in a multidisciplinary diabetes clinic and has the potential to increase DED screening rates in underserved youth, while also being cost-savings to patients and caregivers.
“We have also shown that this system can be implemented in a multidisciplinary diabetes clinic and has the potential to increase DED screening rates in underserved youth, while also being cost-savings to patients and caregivers30,31.”
From Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial (2024) · cites this paper as [30]
Retinopathy screening programs work best in defined populations with government-organized health care, such as UK, Scandinavia, and American Indian tribes, not the general US population.
“Screening programs have been most successful in defined populations with government-organized health care systems, such as in the United Kingdom and Scandinavia and in American Indian tribes rather than in the general population of the United States.503–506”
From American Association of Clinical Endocrinology Clinical Practice Guideline: Developing a Diabetes Mellitus Comprehensive Care Plan-2022 Update. (2022) · cites this paper as [504]
The Abstract and Conclusions discuss screening adherence and point-of-care AI in a U.S. pediatric diabetes clinic, not comparative success of government-organized screening programs. Searches for the United Kingdom, Scandinavia, American Indian tribes, and defined populations found no passage addressing the claimed comparison with the general U.S. population. (LLM, conf=0.75)
Experimental — accuracy not yet measuredAI systems are increasingly in a position to help improve patient and population health outcomes and drive down cost, increase physician job satisfaction, and address health disparities.
“Thus, AI systems are increasingly in a position to help improve patient and population health outcomes and drive down cost, increase physician job satisfaction, and address health disparities20,21,3,22,23.”
From Considerations for addressing bias in artificial intelligence for health equity (2023) · cites this paper as [22]
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Self-citationClosed-Loop, Artificial Intelligence-Based Decision Support Systems, and Data Science
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CHILDSTAR: CHIldren Living With Diabetes See and Thrive with AI Review
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Artificial Intelligence in Pediatric Endocrinology
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TELEOPHTHALMOLOGY: GLOBAL TRENDS
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- Authors
- Wolf, R. M., Liu, T. Y. A., Thomas, C., Prichett, L., Zimmer-Galler, I., Smith, K., Abramoff, M. D., & Channa, R.
- Affiliations
- Johns Hopkins University (US)
- Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA
- University of Wisconsin–Madison (US)
- Corresponding author
- Channa, Roomasa
- ORCID
- Venue
- Diabetes Care
- Publisher
- American Diabetes Association
- Published
- 2021-01-21
- Volume
- 44
- Issue
- 3
- Pages
- 781-787
- ISSN
- 0149-5992 (ISSN-L)
- 1935-5548
- OpenAlex
- W3121831947
- Cited by
- 62
- References
- 40
- Citations by year
- 2026: 8 · 2025: 17 · 2024: 12 · 2023: 12 · 2022: 10 · 2021: 3
- FWCI
- 4.88
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Diabetes Care (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Johns Hopkins Children’s Center Innovation Award
- Research to Prevent Blindness
- 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, AI autonomy, diabetic eye examination, youth with diabetes, pediatric diabetes, screening adherence, screening guidelines adherence, diagnostic accuracy, retinal imaging, health equity
- MeSH terms
- Adolescent, Adult, Artificial Intelligence, Child, Diabetes Mellitus, Diabetic Retinopathy, Humans, Mass Screening, Prospective Studies, Sensitivity and Specificity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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