Artificial intelligence and digital solutions for myopia

Ang, Marcus; Li, Yong; T. Yip, MichelleY; W. Ting, DanielS

doi:10.4103/tjo.tjo-d-23-00032

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Taiwan J Ophthalmol

2023

DOI: 10.4103/tjo.tjo-d-23-00032

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Artificial intelligence and digital solutions for myopia

Marcus Ang,

Yong Li,

MichelleY T. Yip

et al.

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Cited by 4 publications

References 75 publications

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AI-enhanced digital technologies for myopia management: advancements, challenges, and future prospects

Ali,

Zhang,

Guan

et al. 2024

Vis Comput

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AI-enhanced digital technologies for myopia management: advancements, challenges, and future prospects

Ali,

Zhang,

Guan

et al. 2024

Vis Comput

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The Amalgamation of Federated Learning and Explainable Artificial Intelligence for the Internet of Medical Things: A Review

Govardanan,

Murugan,

Yenduri

et al. 2024

RACSC

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The Internet of Medical Things (IoMT) has emerged as a paradigm shift in healthcare, integrating the Internet of Things (IoT) with medical devices, sensors, and healthcare systems. From peripheral devices that monitor vital signs to remote patient monitoring systems and smart hospitals, IoMT provides a vast array of applications that empower healthcare professionals. However, the integration of IoMT presents numerous obstacles, such as data security, privacy concerns, interoperability, scalability, and ethical considerations. For the successful integration and deployment of IoMT, addressing these obstacles is essential. Federated Learning (FL) permits collaborative model training while maintaining data privacy in distributed environments like IoMT. By incorporating Explainable Artificial Intelligence (XAI) techniques, the resulting models become more interpretable and transparent, enabling healthcare professionals to comprehend the underlying decision-making processes. This integration not only improves the credibility of Artificial Intelligence models but also facilitates the detection of biases, errors, and peculiar patterns in the data. The combination of FL and XAI contributes to the development of more privacy-preserving, trustworthy, and explainable AI systems, which are essential for the development of dependable and ethically sound IoMT applications. Hence, the aim of this paper is to conduct a literature review on the amalgamation of FL and XAI for IoMT.

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Bridging the Gap: Exploring Opportunities, Challenges, and Problems in Integrating Assistive Technologies, Robotics, and Automated Machines into the Health Domain

Giansanti

2023

Healthcare

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Artificial intelligence and digital solutions for myopia

Cited by 4 publications

References 75 publications

AI-enhanced digital technologies for myopia management: advancements, challenges, and future prospects

AI-enhanced digital technologies for myopia management: advancements, challenges, and future prospects

The Amalgamation of Federated Learning and Explainable Artificial Intelligence for the Internet of Medical Things: A Review

Bridging the Gap: Exploring Opportunities, Challenges, and Problems in Integrating Assistive Technologies, Robotics, and Automated Machines into the Health Domain

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