AiIoMT: IoMT-Based System-Enabled Artificial Intelligence for Enhanced Smart Healthcare Systems

Awotunde, Joseph Bamidele; Folorunso, Sakinat Oluwabukonla; Ajagbe, Sunday Adeola; Garg, Jatinder; Ajamu, Gbemisola Janet

doi:10.1007/978-3-030-80928-7_10

Cited by 26 publications

(4 citation statements)

References 57 publications

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“…The 5G cloud networking model recommends the data services as per the mode of application demand. Typically, the recent advancements from the IoMT and IoT is recorded in [19][20][21][22] further discussing the influence and challenges in implementing the recommendation models. The extended technological development from augmented AI is discussed in [23] to support the recommendation framework of IoMT devices in smart cities infrastructure.…”

Section: Literature Reviewsmentioning

confidence: 99%

Towards blockchain based federated learning in categorizing healthcare monitoring devices on artificial intelligence of medical things investigative framework

Ahmed,

Mahesh,

Srividhya

et al. 2024

BMC Med Imaging

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Categorizing Artificial Intelligence of Medical Things (AIoMT) devices within the realm of standard Internet of Things (IoT) and Internet of Medical Things (IoMT) devices, particularly at the server and computational layers, poses a formidable challenge. In this paper, we present a novel methodology for categorizing AIoMT devices through the application of decentralized processing, referred to as "Federated Learning" (FL). Our approach involves deploying a system on standard IoT devices and labeled IoMT devices for training purposes and attribute extraction. Through this process, we extract and map the interconnected attributes from a global federated cum aggression server. The aim of this terminology is to extract interdependent devices via federated learning, ensuring data privacy and adherence to operational policies. Consequently, a global training dataset repository is coordinated to establish a centralized indexing and synchronization knowledge repository. The categorization process employs generic labels for devices transmitting medical data through regular communication channels. We evaluate our proposed methodology across a variety of IoT, IoMT, and AIoMT devices, demonstrating effective classification and labeling. Our technique yields a reliable categorization index for facilitating efficient access and optimization of medical devices within global servers.

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Section: Literature Reviewsmentioning

confidence: 99%

Towards blockchain based federated learning in categorizing healthcare monitoring devices on artificial intelligence of medical things investigative framework

Ahmed,

Mahesh,

Srividhya

et al. 2024

BMC Med Imaging

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“…J. B. Awotunde et al [181] suggested a framework based on AIoMT to monitor and diagnose patients and evaluate the model's accuracy. Integrating AI and IoMT in the healthcare industry can alleviate the burden of medical processes and significantly enhance disease diagnosis, prediction, treatment, screening, and medication.…”

Section: Smart Healthcarementioning

confidence: 99%

Integration of IoT-Enabled Technologies and Artificial Intelligence (AI) for Smart City Scenario: Recent Advancements and Future Trends

Alahi

Sukkuea

Tina

et al. 2023

Sensors

147

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As the global population grows, and urbanization becomes more prevalent, cities often struggle to provide convenient, secure, and sustainable lifestyles due to the lack of necessary smart technologies. Fortunately, the Internet of Things (IoT) has emerged as a solution to this challenge by connecting physical objects using electronics, sensors, software, and communication networks. This has transformed smart city infrastructures, introducing various technologies that enhance sustainability, productivity, and comfort for urban dwellers. By leveraging Artificial Intelligence (AI) to analyze the vast amount of IoT data available, new opportunities are emerging to design and manage futuristic smart cities. In this review article, we provide an overview of smart cities, defining their characteristics and exploring the architecture of IoT. A detailed analysis of various wireless communication technologies employed in smart city applications is presented, with extensive research conducted to determine the most appropriate communication technologies for specific use cases. The article also sheds light on different AI algorithms and their suitability for smart city applications. Furthermore, the integration of IoT and AI in smart city scenarios is discussed, emphasizing the potential contributions of 5G networks coupled with AI in advancing modern urban environments. This article contributes to the existing literature by highlighting the tremendous opportunities presented by integrating IoT and AI, paving the way for the development of smart cities that significantly enhance the quality of life for urban dwellers while promoting sustainability and productivity. By exploring the potential of IoT, AI, and their integration, this review article provides valuable insights into the future of smart cities, demonstrating how these technologies can positively impact urban environments and the well-being of their inhabitants.

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“…Nicolae and coworkers developed a machine learning (ML)-based prostate implant planning system that decreased treatment planning time to (2.38 ± 0.96) minutes and provided clinical treatment decision support for prostate cancer (42). Bamidele et al suggested an IoMT-based intelligent health monitoring system that can provide individualized therapy recommendations and increase breast cancer patients' survival time (43).…”

Section: Support For Treatment Decisionsmentioning

confidence: 99%

Artificial Intelligence and Medicine

Vanak

2022

Sci Insights

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The introduction of artificial intelligence (AI) has resulted in numerous technological advancements in the medical profession and a radical transformation of the old medical model. Artificial intelligence in medicine consists mostly of machine learning, deep learning, expert systems, intelligent robotics, the internet of medical things, and other prevalent and new AI technology. The primary applications of AI in the medical industry are intelligent screening, intelligent diagnosis, risk prediction, and supplemental treatment. Presently, medical AI has achieved significant advances, and big data quality management, new technology empowerment innovation, multi-domain knowledge integration, and personalized medical decision-making will exhibit greater growth potential in the clinical arena.

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AiIoMT: IoMT-Based System-Enabled Artificial Intelligence for Enhanced Smart Healthcare Systems

Cited by 26 publications

References 57 publications

Towards blockchain based federated learning in categorizing healthcare monitoring devices on artificial intelligence of medical things investigative framework

Towards blockchain based federated learning in categorizing healthcare monitoring devices on artificial intelligence of medical things investigative framework

Integration of IoT-Enabled Technologies and Artificial Intelligence (AI) for Smart City Scenario: Recent Advancements and Future Trends

Artificial Intelligence and Medicine

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