Collaborative Care for Diabetic Retinopathy: Integrating Artificial Intelligence and Clinical Pharmacy Services – A Comprehensive Review

[{“box”:0,”content”:”n[if 992 equals=”Open Access”]n

n

n

n

Open Access

nn

n

n[/if 992]n[if 2704 equals=”Yes”]n

n

Notice

nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n

n[/if 2704]n

n

Year : 2025 [if 2224 equals=””]03/10/2025 at 10:14 PM[/if 2224] | [if 1553 equals=””] Volume : 15 [else] Volume : 15[/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03 | Page :

n

n

nn

n

n

n

    By

    n

    [foreach 286]n

    n

    A Shannumukha Sainath, R Deepak Kumar Reddy, B Keshavaradhan, C. Sai Keerthi, Karthik Manikanta, M. Suneetha,

    n t

  • n

    n[/foreach]

    n

n[if 2099 not_equal=”Yes”]n

    [foreach 286] [if 1175 not_equal=””]n t

  1. Assistant Professor, Pharm.D Interns, Pharm.D Interns, Pharm.D Interns, Pharm.D Interns, Pharm.D Interns, Department of Pharmacy Practice, Nirmala College of Pharmacy – Autonomous, Kadapa, Department of Pharmacy Practice, Nirmala College of Pharmacy – Autonomous, Kadapa, Department of Pharmacy Practice, Nirmala College of Pharmacy – Autonomous, Kadapa, Department of Pharmacy Practice, Nirmala College of Pharmacy – Autonomous, Kadapa, Department of Pharmacy Practice, Nirmala College of Pharmacy – Autonomous, Kadapa, Department of Pharmacy Practice, Nirmala College of Pharmacy – Autonomous, Kadapa, Andhra Pradesh, Andhra Pradesh, Andhra Pradesh, Andhra Pradesh, Andhra Pradesh, Andhra Pradesh, India, India, India, India, India, India

    2. n[/if 1175][/foreach]

n[/if 2099][if 2099 equals=”Yes”][/if 2099]n

n

Abstract

n

Edit

n

nBackground: Diabetic retinopathy (DR) remains the leading cause of blindness among working-age adults globally, affecting approximately 103 million people worldwide. The integration of artificial intelligence (AI) technologies with clinical pharmacy services presents unprecedented opportunities to enhance screening, diagnosis, and management of DR through collaborative care models. Objective: This comprehensive review examines the current landscape of collaborative care approaches for diabetic retinopathy management, focusing on the integration of AI-powered diagnostic tools with clinical pharmacy services to improve patient outcomes and healthcare delivery efficiency. Methods: A systematic literature review was conducted using PubMed, Cochrane Library, and EMBASE databases from 2018-2024, focusing on studies investigating AI applications in DR screening, clinical pharmacy interventions in diabetes care, and collaborative care models. Results: Evidence demonstrates that AI-powered screening tools achieve sensitivity rates of 85-95% for detecting referable DR, while clinical pharmacy interventions improve glycemic control by 0.5-1.2% HbA1c reduction. Collaborative care models integrating both approaches show superior outcomes compared to traditional care pathways. Conclusion: The synergistic integration of AI technologies with clinical pharmacy services represents a paradigm shift toward more efficient, accessible, and patient-centered care for diabetic retinopathy management.nn

n

n

n

Keywords: Diabetic retinopathy, Artificial intelligence, Clinical pharmacy, Collaborative care, Telemedicine, Diabetes management

n[if 424 equals=”Regular Issue”][This article belongs to Research and Reviews: A Journal of Pharmacology ]

n

[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Research and Reviews: A Journal of Pharmacology (rrjop)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

n

n

n

How to cite this article:
nA Shannumukha Sainath, R Deepak Kumar Reddy, B Keshavaradhan, C. Sai Keerthi, Karthik Manikanta, M. Suneetha. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]Collaborative Care for Diabetic Retinopathy: Integrating Artificial Intelligence and Clinical Pharmacy Services – A Comprehensive Review[/if 2584]. Research and Reviews: A Journal of Pharmacology. 03/10/2025; 15(03):-.

n

How to cite this URL:
nA Shannumukha Sainath, R Deepak Kumar Reddy, B Keshavaradhan, C. Sai Keerthi, Karthik Manikanta, M. Suneetha. [if 2584 equals=”][226 striphtml=1][else]Collaborative Care for Diabetic Retinopathy: Integrating Artificial Intelligence and Clinical Pharmacy Services – A Comprehensive Review[/if 2584]. Research and Reviews: A Journal of Pharmacology. 03/10/2025; 15(03):-. Available from: https://journals.stmjournals.com/rrjop/article=03/10/2025/view=0

nn

n

n[if 992 equals=”Open Access”]Full Text PDF[/if 992]n

n

n[if 992 not_equal=”Open Access”]n

n

n[/if 992]n

nn

nnn

n[if 379 not_equal=””]nn

Browse Figures

n

n

n[foreach 379]

figures

[/foreach]n

n

n

n[/if 379]

n

n

Edit

n

n

n

References n

n[if 1104 equals=””]n

1. Yau JW, Rogers SL, Kawasaki R, et al. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care. 2012;35(3):556-64.
2. International Diabetes Federation. IDF Diabetes Atlas. 10th ed. Brussels: International Diabetes Federation; 2021.
3. Stitt AW, Curtis TM, Chen M, et al. The progress in understanding and treatment of diabetic retinopathy. Prog Retin Eye Res. 2016;51:156-86.
4. Congdon NG, Friedman DS, Lietman T. Important causes of visual impairment in the world today. JAMA. 2003;290(15):2057-60.
5. American Diabetes Association. Standards of Medical Care in Diabetes-2023. Diabetes Care. 2023;46(Suppl 1):S1-S291.
6. Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402-10.
7. Chisholm-Burns MA, Kim Lee J, Spivey CA, et al. US pharmacists’ effect as team members on patient care: systematic review and meta-analyses. Med Care. 2010;48(10):923-33.
8. Collins CD, Greco AJ, Maldonado DR, et al. Impact of artificial intelligence on clinical pharmacy practice. Am J Health Syst Pharm. 2021;78(11):945-51.
9. Antonetti DA, Klein R, Gardner TW. Diabetic retinopathy. N Engl J Med. 2012;366(13):1227-39.
10. Hammes HP, Feng Y, Pfister F, Brownlee M. Diabetic retinopathy: targeting vasoregression. Diabetes. 2011;60(1):9-16.
11. Roy S, Kern TS, Song B, Stuebe C. Mechanistic insights into pathological changes in the diabetic retina: implications for targeting diabetic retinopathy. Am J Pathol. 2017;187(1):9-19.
12. Wilkinson CP, Ferris FL 3rd, Klein RE, et al. Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology. 2003;110(9):1677-82.
13. Early Treatment Diabetic Retinopathy Study Research Group. Grading diabetic retinopathy from stereoscopic color fundus photographs–an extension of the modified Airlie House classification. ETDRS report number 10. Ophthalmology. 1991;98(5 Suppl):786-806.
14. Aiello LP, Avery RL, Arrigg PG, et al. Vascular endothelial growth factor in ocular fluid of patients with diabetic retinopathy and other retinal disorders. N Engl J Med. 1994;331(22):1480-7.
15. Gross JG, Glassman AR, Jampol LM, et al. Panretinal photocoagulation vs intravitreous ranibizumab for proliferative diabetic retinopathy: a randomized clinical trial. JAMA. 2015;314(20):2137-46.
16. Klein R, Klein BE, Moss SE, et al. The Wisconsin epidemiologic study of diabetic retinopathy. IV. Diabetic macular edema. Ophthalmology. 1984;91(12):1464-74.
17. Sambhav K, Grover S, Chalam KV. The application of optical coherence tomography angiography in retinal diseases. Surv Ophthalmol. 2017;62(6):838-66.
18. Sun JK, Radwan SH, Soliman AZ, et al. Neural retinal disorganization as a robust marker of visual acuity in current and resolved diabetic macular edema. Diabetes. 2015;64(7):2560-70.
19. Association of American Medical Colleges. The complexities of physician supply and demand: projections from 2018 to 2033. Washington, DC: AAMC; 2020.
20. American Academy of Ophthalmology. The ophthalmic workforce: a study of the career paths of ophthalmologists. San Francisco: AAO; 2019.
21. Zhang X, Saaddine JB, Chou CF, et al. Prevalence of diabetic retinopathy in the United States, 2005-2008. JAMA. 2010;304(6):649-56.
22. Chou CF, Barker LE, Crews JE, et al. Disparities in eye care utilization among the United States adults with visual impairment: findings from the behavioral risk factor surveillance system 2006-2009. Am J Ophthalmol. 2012;154(6 Suppl):S45-52.e1.
23. Bressler NM, Varma R, Doan QV, et al. Underuse of the health care system by persons with diabetes mellitus and diabetic macular edema in the United States. JAMA Ophthalmol. 2014;132(2):168-73.
24. Fong DS, Aiello L, Gardner TW, et al. Retinopathy in diabetes. Diabetes Care. 2004;27 Suppl 1:S84-7.
25. Litvin CB, Ornstein SM, Wessell AM, et al. Adoption of a clinical decision support system to promote judicious use of antibiotics for acute respiratory infections in primary care. Int J Med Inform. 2012;81(8):521-6.
26. García-Pérez LE, Alvarez M, Dilla T, et al. Adherence to therapies in patients with type 2 diabetes. Diabetes Ther. 2013;4(2):175-94.
27. Ting DSW, Pasquale LR, Peng L, et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2019;103(2):167-75.
28. Schmidt-Erfurth U, Sadeghipour A, Gerendas BS, et al. Artificial intelligence in retina. Prog Retin Eye Res. 2018;67:1-29.
29. Kaggle Inc. Diabetic retinopathy detection [Internet]. 2015 [cited 2024 Jan 31]. Available from: https://www.kaggle.com/c/diabetic-retinopathy-detection
30. De Fauw J, Ledsam JR, Romera-Paredes B, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med. 2018;24(9):1342-50.
31. Abràmoff MD, Lavin PT, Birch M, et al. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med. 2018;1:39.
32. Van der Heijden AA, Abramoff MD, Verbraak F, et al. Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the Hoorn Diabetes Care System. Acta Ophthalmol. 2018;96(1):63-8.
33. Raumviboonsuk P, Krause J, Chotcomwongse P, et al. Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program. NPJ Digit Med. 2019;2:25.
34. Li Z, Keel S, Liu C, et al. An automated grading system for detection of vision-threatening referable diabetic retinopathy on the basis of color fundus photographs. Diabetes Care. 2018;41(12):2509-16.
35. Wubben DP, Vivian EM. Effects of pharmacist outpatient interventions on adults with diabetes mellitus: a systematic review. Pharmacotherapy. 2008;28(4):421-36.
36. Hirsch JD, Steers N, Adler DS, et al. Primary care-based, pharmacist-physician collaborative medication-therapy management of hypertension: a randomized, pragmatic trial. Clin Ther. 2014;36(9):1244-54.
37. Cranor CW, Bunting BA, Christensen DB. The Asheville Project: long-term clinical and economic outcomes of a community pharmacy diabetes care program. J Am Pharm Assoc (Wash). 2003;43(2):173-84.
38. Machado M, Bajcar J, Guzzo GC, et al. Sensitivity of patient outcomes to pharmacist interventions. Part I: systematic review and meta-analysis in diabetes management. Ann Pharmacother. 2007;41(10):1569-82.
39. Pande S, Hiller JE, Nkansah N, et al. The effect of pharmacist-provided non-dispensing services on patient outcomes, health service utilisation and costs in low- and middle-income countries. Cochrane Database Syst Rev. 2013;(2):CD010398.
40. Sim DA, Keane PA, Mehta H, et al. Artificial intelligence in diabetic retinopathy screening. Eye (Lond). 2020;34(3):451-60.
41. Bellemo V, Burlina P, Yong Liu T, et al. Artificial intelligence screening for diabetic retinopathy: the real-world emerging application. Curr Diab Rep. 2019;19(9):72.
42. Rajalakshmi R, Subashini R, Anjana RM, et al. Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence. Eye (Lond). 2018;32(6):1138-44.
43. Hansen MB, Torbeck-Hansen N, Sofie Ellegård Nielsen A, et al. Developing an automated diabetic retinopathy screening program: economic evaluation of the implementation of artificial intelligence in Danish diabetes clinics. Acta Diabetol. 2022;59(2):151-60.
44. Dutta S, Manideep BC, Bhaskaranand M, et al. An artificial intelligence-enabled retina screening system (EyeArt) for diabetic retinopathy screening: a potential tool for improving compliance to diabetic retinopathy screening. Diabetes Res Clin Pract. 2018;144:225-30.
45. Ruamviboonsuk P, Tiwari R, Sayres R, et al. Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort study. Lancet Digit Health. 2022;4(4):e235-44.
46. Wolf RM, Channa R, Abramoff MD, et al. Cost-effectiveness of autonomous point-of-care diabetic retinopathy screening for pediatric patients with diabetes. JAMA Ophthalmol. 2020;138(10):1063-9.
47. Beede E, Baylor E, Hersch F, et al. A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems; 2020.
48. American Pharmacists Association. Certificate Training Programs in Diabetes Care. Washington, DC: APhA; 2021.

nn[/if 1104][if 1104 not_equal=””]n

    [foreach 1102]n t

  1. [if 1106 equals=””], [/if 1106][if 1106 not_equal=””],[/if 1106]

    2. n[/foreach]

n[/if 1104]

n

nn[if 1114 equals=”Yes”]n

n[/if 1114]

n

n

[if 424 not_equal=””]Regular Issue[else]Published[/if 424] Subscription Review Article

n

n

[if 2146 equals=”Yes”][/if 2146][if 2146 not_equal=”Yes”][/if 2146]n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n[if 1748 not_equal=””]

[else]

[/if 1748]n

n[if 1746 equals=”Retracted”]n

n

n

n

[/if 1746]n[if 4734 not_equal=””]

n

n

n

[/if 4734]n

n

Volume 15
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03
Received 03/07/2025
Accepted 24/07/2025
Published 03/10/2025
Retracted
Publication Time 92 Days

n

n

75

nn

n

Login

n
My IP
n

PlumX Metrics

nn

n

n

n[if 1746 equals=”Retracted”]n

[/if 1746]nnn

nnn”}]