Leveraging Artificial Intelligence and Internet of Things for Advancements in Medical Imaging: Applications, Challenges, and Future Directions

Abstract

The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) has revolutionized medical imaging by introducing unprecedented levels of accuracy, efficiency, and scalability to diagnostic processes. Advanced AI methodologies, including convolutional neural networks (CNNs), fully convolutional networks (FCNs), and generative adversarial networks (GANs), have enabled precise image classification, segmentation, detection, and registration, addressing complex challenges across diverse medical domains. IoT technologies complement these advancements by facilitating real-time data acquisition, seamless connectivity, and remote accessibility, making advanced healthcare solutions available even in resource-limited settings. This synergistic interplay has empowered clinicians with early disease detection capabilities, personalized treatment strategies, and optimized clinical workflows, ultimately enhancing patient outcomes. Despite these achievements, critical challenges such as data privacy, interoperability, and computational resource constraints persist, requiring innovative solutions. This paper presents a comprehensive review of AI and IoT applications in medical imaging, identifies existing limitations, and proposes a strategic framework for the development and deployment of these technologies to shape the future of global healthcare. Ultimately, this study demonstrates that the fusion of AI and IoT in medical imaging not only improves diagnostic accuracy and efficiency but also contributes to significant advancements in treatment processes and medical education.

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