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Skip search results from other journals and go to results- 2 JMIR Research Protocols
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These systems can integrate existing DRTS pathways in 2 ways: a semiautomated manner, where they replace the preliminary triage currently performed by level 1 trained graders [14]. Alternatively, they can operate in a fully autonomous way, which would not require any human oversight [15].
In this study, we share findings from incorporating a semiautomated AI model into the care strategy for diabetic patients at a major tertiary care center in Quebec.
JMIR Diabetes 2024;9:e59867
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All 4 countries are members of the International Medical Regulators Device Forum and have a track record of admitting AIa MD to their markets. No restrictions will be placed on the type of ophthalmic imaging modality involved or the intended use of the AIa MD.
The AIa MD will have a partial or fully data-led mechanism (eg, regression modeling, random forest, or convolutional neural networks).
JMIR Res Protoc 2024;13:e52602
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Democratizing Artificial Intelligence Imaging Analysis With Automated Machine Learning: Tutorial
Machine learning (ML) is a form of AI that describes the computational process of leveraging data to improve performance in a defined task, thereby developing sophisticated models without explicit programming. More recently, deep learning (DL) has emerged as a powerful form of ML capable of interpreting unstructured data, such as images, language, and speech [2,3].
J Med Internet Res 2023;25:e49949
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An example of such a tool is a classification algorithm that distinguishes retinal photographs containing signs of diabetic retinopathy from those that do not [14]. The tool “learned” to do this in a relatively unexplainable fashion through exposure to a great quantity of retinal imaging data accompanied by human-expert labels of whether diabetic retinopathy was present.
J Med Internet Res 2023;25:e39742
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Despite interest and investment from academia, industry, and policy makers, a notable paucity of real-world applications of AI-enabled CCDSTs persists [6]. This is a mark of a translational gap known as the “AI chasm” [7].
To address this AI chasm, there is a need for contemporary evidence syntheses of clinical AI research, the quantitative aspects of which have already been satisfied [8-10].
JMIR Res Protoc 2022;11(4):e33145
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