Machine Learning in Biomedical Implants: MATLAB Applications and Future Directions

Abstract

The integration of machine learning (ML) techniques with biomedical implant systems has markedly transformed patient care, diagnostics, and therapy. MATLAB, esteemed for its robust computational capabilities and extensive toolboxes, has emerged as a preferred platform for the development and implementation of ML algorithms in biomedical applications. This review examines the current state of ML implementation using MATLAB across various biomedical implant applications, including neural prosthetics, cardiac implants, orthopedic devices, and continuous monitoring systems. We discuss key ML algorithms, such as support vector machines, neural networks, random forests, and deep learning architectures, which have been successfully implemented in MATLAB for signal processing, pattern recognition, and predictive analytics in implantable devices. This review also addresses challenges such as data quality, computational constraints, regulatory compliance, and real-time processing requirements. Future directions emphasize the need for edge computing integration, federated learning approaches, and enhanced interpretability of ML models in clinical settings. This comprehensive analysis provides researchers and practitioners with insights into leveraging MATLAB to advance intelligent biomedical implant technologies.

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