Integration of artificial intelligence algorithms with bioelectrical signal processing has the potential to enhance diagnostic precision and improve patient outcomes in personalized medicine.
The integration of artificial intelligence with bioelectrical signals such as ECG and EEG holds significant potential for improving real-time monitoring and diagnostic precision in personalized medicine.
This review examines the role of various bioelectrical signals in conjunction with artificial intelligence (AI) and analyzes how these signals are utilized in AI applications. The applications of electroencephalography (EEG), electroretinography (ERG), electromyography (EMG), electrooculography (EOG), and electrocardiography (ECG) in diagnostic and therapeutic systems are focused on. Signal processing techniques are discussed, and relevant studies that have utilized these signals in various clinical and research settings are highlighted. Advances in signal processing and classification methodologies powered by AI have significantly improved accuracy and efficiency in medical analysis. The integration of AI algorithms with bioelectrical signal processing for real-time monitoring and diagnosis, particularly in personalized medicine, is emphasized. AI-driven approaches are shown to have the potential to enhance diagnostic precision and improve patient outcomes. However, further research is needed to optimize these models for diverse clinical environments and fully exploit the interaction between bioelectrical signals and AI technologies.
Alfonso et al. (Fri,) conducted a review in Diagnostic and therapeutic systems. Artificial intelligence and bioelectrical signal processing was evaluated. Integration of artificial intelligence algorithms with bioelectrical signal processing has the potential to enhance diagnostic precision and improve patient outcomes in personalized medicine.
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