Publication: Economic Evaluations of Artificial Intelligence in Ophthalmology
Issued Date
2021-07-13
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ISSN
21620989
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2-s2.0-85114201178
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Mahidol University
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SCOPUS
Bibliographic Citation
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.). Vol.10, No.3 (2021), 307-316
Suggested Citation
Paisan Ruamviboonsuk, Somporn Chantra, Kasem Seresirikachorn, Varis Ruamviboonsuk, Sermsiri Sangroongruangsri Economic Evaluations of Artificial Intelligence in Ophthalmology. Asia-Pacific journal of ophthalmology (Philadelphia, Pa.). Vol.10, No.3 (2021), 307-316. doi:10.1097/APO.0000000000000403 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/78034
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Title
Economic Evaluations of Artificial Intelligence in Ophthalmology
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Abstract
ABSTRACT: Artificial intelligence (AI) is expected to cause significant medical quality enhancements and cost-saving improvements in ophthalmology. Although there has been a rapid growth of studies on AI in the recent years, real-world adoption of AI is still rare. One reason may be because the data derived from economic evaluations of AI in health care, which policy makers used for adopting new technology, have been fragmented and scarce. Most data on economics of AI in ophthalmology are from diabetic retinopathy (DR) screening. Few studies classified costs of AI software, which has been considered as a medical device, into direct medical costs. These costs of AI are composed of initial and maintenance costs. The initial costs may include investment in research and development, and costs for validation of different datasets. Meanwhile, the maintenance costs include costs for algorithms upgrade and hardware maintenance in the long run. The cost of AI should be balanced between manufacturing price and reimbursements since it may pose significant challenges and barriers to providers. Evidence from cost-effectiveness analyses showed that AI, either standalone or used with humans, was more cost-effective than manual DR screening. Notably, economic evaluation of AI for DR screening can be used as a model for AI to other ophthalmic diseases.
