Is Artificial Intelligence the Cost-Saving Lens to Diabetic Retinopathy Screening in Low- and Middle-Income Countries?

Anza Rizvi; Fatima Rizvi; Parth Lalakia; Leslie Hyman; Rosemary Frasso; Les Sztandera; Anthony Vipin Das

doi:10.7759/cureus.45539

Is Artificial Intelligence the Cost-Saving Lens to Diabetic Retinopathy Screening in Low- and Middle-Income Countries?

Cureus. 2023 Sep 19;15(9):e45539. doi: 10.7759/cureus.45539. eCollection 2023 Sep.

Authors

Anza Rizvi^{1

2}, Fatima Rizvi^{1

2}, Parth Lalakia^{2

3

4}, Leslie Hyman^{5

6}, Rosemary Frasso^{1

2

7}, Les Sztandera⁸, Anthony Vipin Das⁹

Affiliations

¹ Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, USA.
² College of Population Health, Thomas Jefferson University, Philadelphia, USA.
³ Osteopathic Medicine, Rowan-Virtua School of Osteopathic Medicine, Stratford, USA.
⁴ Office of Global Affairs, Thomas Jefferson University, Philadelphia, USA.
⁵ Geriatric Medicine and Palliative Care, Department of Family Medicine, Thomas Jefferson University, Philadelphia, USA.
⁶ The Vickie and Jack Farber Vision Research Center, Wills Eye Hospital, Philadelphia, USA.
⁷ Asano-Gonnella Center for Research in Medical Education and Health Care, Thomas Jefferson University, Philadelphia, USA.
⁸ Kanbar College of Design, Engineering, and Commerce, Thomas Jefferson University, Philadelphia, USA.
⁹ Ophthalmology, LV Prasad Eye Institute, Hyderabad, IND.

Abstract

Diabetes is a rapidly growing global health crisis disproportionately affecting low- and middle-income countries (LMICs). The emergence of diabetes as a global pandemic is one of the major challenges to human health, as long-term microvascular complications such as diabetic retinopathy (DR) can lead to irreversible blindness. Leveraging artificial intelligence (AI) technology may improve the diagnostic accuracy, efficiency, and accessibility of DR screenings across LMICs. However, there is a gap between the potential of AI technology and its implementation in clinical practice. The main objective of this systematic review is to summarize the currently available literature on the health economic assessments of AI implementation for DR screening in LMICs. The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. We conducted an extensive systematic search of PubMed/MEDLINE, Scopus, and the Web of Science on July 15, 2023. Our review included full-text English-language articles from any publication year. The Joanna Briggs Institute's (JBI) critical appraisal checklist for economic evaluations was used to rate the quality and rigor of the selected articles. The initial search generated 1,423 records and was narrowed to five full-text articles through comprehensive inclusion and exclusion criteria. Of the five articles included in our systematic review, two used a cost-effectiveness analysis, two used a cost-utility analysis, and one used both a cost-effectiveness analysis and a cost-utility analysis. Across the five articles, LMICs such as China, Thailand, and Brazil were represented in the economic evaluations and models. Overall, three out of the five articles concluded that AI-based DR screening was more cost-effective in comparison to standard-of-care screening methods. Our systematic review highlights the need for more primary health economic analyses that carefully evaluate the economic implications of adopting AI technology for DR screening in LMICs. We hope this systematic review will offer valuable guidance to healthcare providers, scientists, and legislators to support appropriate decision-making regarding the implementation of AI algorithms for DR screening in healthcare workflows.

Keywords: ai & robotics in healthcare; diabetic retinopathy screening; health economic; low- and middle-income countries; ophthalmology.

Publication types

Review

Grants and funding

WT_/Wellcome Trust/United Kingdom