Integrating artificial intelligence into biomedical signal analysis represents a significant breakthrough in enhancing the precision and efficiency of disease diagnostics and therapeutics.
Abstract Integrating artificial intelligence (AI) into biomedical signal analysis represents a significant breakthrough in enhanced precision and efficiency of disease diagnostics and therapeutics. From traditional computational models to advanced machine learning algorithms, AI technologies have improved signal processing by efficiently handling complexity and interpreting intricate datasets. Understanding physiological data, which requires highly trained professionals, is now more accessible; in regions with limited access, AI tools expand healthcare accessibility by providing high-level diagnostic insights, ultimately improving health outcomes. This review explores various AI methodologies, including supervised, unsupervised, and reinforcement learning, and examines their synergy for biomedical signal analysis and future directions in medical science. By capturing a comprehensive overview of the current state and prospects of AI-driven healthcare, this paper highlights the transformative potential of AI in analyzing biomedical signals. Graphical Abstract
Lee et al. (Tue,) conducted a review in Biomedical signal analysis. Artificial intelligence was evaluated. Integrating artificial intelligence into biomedical signal analysis represents a significant breakthrough in enhancing the precision and efficiency of disease diagnostics and therapeutics.
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