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Implementation of Artificial Intelligence–Based Diabetic Retinopathy Screening in a Tertiary Care Hospital in Quebec: Prospective Validation Study

Implementation of Artificial Intelligence–Based Diabetic Retinopathy Screening in a Tertiary Care Hospital in Quebec: Prospective Validation Study

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.

Fares Antaki, Imane Hammana, Marie-Catherine Tessier, Andrée Boucher, Maud Laurence David Jetté, Catherine Beauchemin, Karim Hammamji, Ariel Yuhan Ong, Marc-André Rhéaume, Danny Gauthier, Mona Harissi-Dagher, Pearse A Keane, Alfons Pomp

JMIR Diabetes 2024;9:e59867

AI as a Medical Device for Ophthalmic Imaging in Europe, Australia, and the United States: Protocol for a Systematic Scoping Review of Regulated Devices

AI as a Medical Device for Ophthalmic Imaging in Europe, Australia, and the United States: Protocol for a Systematic Scoping Review of Regulated Devices

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).

Ariel Yuhan Ong, Henry David Jeffry Hogg, Aditya U Kale, Priyal Taribagil, Ashley Kras, Eliot Dow, Trystan Macdonald, Xiaoxuan Liu, Pearse A Keane, Alastair K Denniston

JMIR Res Protoc 2024;13:e52602

Democratizing Artificial Intelligence Imaging Analysis With Automated Machine Learning: Tutorial

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].

Arun James Thirunavukarasu, Kabilan Elangovan, Laura Gutierrez, Yong Li, Iris Tan, Pearse A Keane, Edward Korot, Daniel Shu Wei Ting

J Med Internet Res 2023;25:e49949

Stakeholder Perspectives of Clinical Artificial Intelligence Implementation: Systematic Review of Qualitative Evidence

Stakeholder Perspectives of Clinical Artificial Intelligence Implementation: Systematic Review of Qualitative Evidence

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.

Henry David Jeffry Hogg, Mohaimen Al-Zubaidy, Technology Enhanced Macular Services Study Reference Group, James Talks, Alastair K Denniston, Christopher J Kelly, Johann Malawana, Chrysanthi Papoutsi, Marion Dawn Teare, Pearse A Keane, Fiona R Beyer, Gregory Maniatopoulos

J Med Internet Res 2023;25:e39742

Stakeholder Perspectives on Clinical Decision Support Tools to Inform Clinical Artificial Intelligence Implementation: Protocol for a Framework Synthesis for Qualitative Evidence

Stakeholder Perspectives on Clinical Decision Support Tools to Inform Clinical Artificial Intelligence Implementation: Protocol for a Framework Synthesis for Qualitative Evidence

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].

Mohaimen Al-Zubaidy, HD Jeffry Hogg, Gregory Maniatopoulos, James Talks, Marion Dawn Teare, Pearse A Keane, Fiona R Beyer

JMIR Res Protoc 2022;11(4):e33145

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