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Considering the Clinical Significance of Artificial Intelligence and Biosensors in the Healthcare Sector: A Review
7
Zitationen
3
Autoren
2024
Jahr
Abstract
Over the last decade, medical imaging, wearable sensors, personal health records, and public health groups have increased medical research data. This data may be used by cloud computing, GPUs, FPGAs, and TPUs. Several powerful AI algorithms have been created to analyze healthcare's massive databases. Health, biology, biosensors, and AI advances are covered. We address precision medicine, medical imaging, and IoT biosensors with machine learning. We study the newest wearable biosensing tech. Modern gadgets detect ailments using AI to examine electrochemical and electrophysiological data. These innovations show customized medicine by offering accurate, efficient, and economical point-of-care therapy. In healthcare data, edge computing, quick AI, and federated learning are examined. We finish with data-driven AI, IoT healthcare and biosensors, and data modality distribution adjustments. My last sentence is future thinking.
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