Economic challenges of artificial intelligence adoption for diabetic retinopathy
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Challenges such as decreased performance on unseen imaging modalities and the commercially available AI diagnosis, which is difficult for medical insurance systems to cover, need to be addressed.
“However, challenges such as decreased performance on unseen imaging modalities and the commercially available AI diagnosis, which is difficult for medical insurance systems to cover,8 need to be addressed”
From Using artificial intelligence to improve human performance: efficient retinal disease detection training with synthetic images (2024) · cites this paper as [8]
Cites
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Characterizing the clinical adoption of medical AI through U.S. insurance claimsPreprint
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- Authors
- Chen, E. M., Chen, D., Chilakamarri, P., Lopez, R., & Parikh, R.
- Affiliations
- Yale University (US)
- NYU Langone Health (US)
- University of Pennsylvania (US)
- Corresponding author
- Parikh, Ravi
- Venue
- Ophthalmology
- Publisher
- Elsevier BV
- Published
- 2021-03
- Volume
- 128
- Issue
- 3
- Pages
- 475-477
- ISSN
- 0161-6420 (ISSN-L)
- OpenAlex
- W3044376133
- Cited by
- 21
- References
- 6
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 5 · 2023: 5 · 2022: 2 · 2021: 2
- FWCI
- 1.07
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Ophthalmology (publishedVersion)
- PubMed (publishedVersion)
- 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, artificial intelligence, artificial intelligence adoption, economic barriers, diabetes screening, health insurance, Affordable Care Act, Medicaid expansion, blindness prevention
- MeSH terms
- Artificial Intelligence, Diabetic Retinopathy, Diagnostic Techniques, Ophthalmological, Medicare, Humans, Ophthalmology, Reimbursement Mechanisms, Sensitivity and Specificity, United States
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