Health Technology/clinical-trials

Pragmatic Trial Tests AI-Driven Diabetic Retinopathy Screening in Primary Care — Implementation Study Targets 1,700 Patients

247 Health News Editorial

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3 May 2026

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4 min read

·clinical-trials

A pragmatic implementation trial is testing whether AI-powered diabetic retinopathy screening in primary care settings improves patient adherence to eye examinations compared to usual care. The study targets 1,700 patients across four US clinics, with results expected in 2027.

Originally reported by ClinicalTrials.gov

References

  1. 1.University of Wisconsin, Madison. (2024). Implementing Artificial Intelligence (AI) to Prevent Vision Loss From Diabetes [Clinical trial registration NCT07559292]. ClinicalTrials.gov. https://clinicaltrials.gov/study/NCT07559292
  2. 2.Teo, Z. L., Tham, Y. C., Yu, M., Chee, M. L., Rim, T. H., Cheung, N., Bikbov, M. M., Wang, Y. X., Tang, Y., Lu, Y., Wong, I. Y., Ting, D. S. W., Saw, S. M., Cheng, C. Y., & Wong, T. Y. (2021). Global prevalence of diabetic retinopathy and projection of burden through 2045: Systematic review and meta-analysis. Ophthalmology, 128(11), 1580-1591. https://doi.org/10.1016/j.ophtha.2021.04.027
  3. 3.Abràmoff, M. D., Lavin, P. T., Birch, M., Shah, N., & Folk, J. C. (2018). Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digital Medicine, 1, 39. https://doi.org/10.1038/s41746-018-0040-6
  4. 4.Solomon, S. D., Chew, E., Duh, E. J., Sobrin, L., Sun, J. K., VanderBeek, B. L., Wykoff, C. C., & Gardner, T. W. (2017). Diabetic retinopathy: A position statement by the American Diabetes Association. Diabetes Care, 40(3), 412-418. https://doi.org/10.2337/dc16-2641
  5. 5.Mansberger, S. L., Gleitsmann, K., Gardiner, S., Sheppler, C., Demirel, S., Wooten, K., & Becker, T. M. (2013). Comparing the effectiveness of telemedicine and traditional surveillance in providing diabetic retinopathy screening examinations: A randomized controlled trial. Archives of Ophthalmology, 131(1), 63-69. https://doi.org/10.1001/archophthalmol.2013.4055
  6. 6.Ting, D. S. W., Pasquale, L. R., Peng, L., Campbell, J. P., Lee, A. Y., Raman, R., Tan, G. S. W., Schmetterer, L., Keane, P. A., & Wong, T. Y. (2019). Artificial intelligence and deep learning in ophthalmology. British Journal of Ophthalmology, 103(2), 167-175. https://doi.org/10.1136/bjophthalmol-2018-313173
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