Advancement of machine learning algorithms in biosensors

Biosensors have emerged as transformative tools in modern diagnostics, enabling rapid, accurate, and sensitive detection of biological markers for disease diagnosis, real-time monitoring, and personalized healthcare. However, current biosensors still exhibit gaps in accuracy and sensitivity for various diagnostics applications, particularly in the detection of multiple analytes, non-invasive sensing, signal noise interference, and ultra-low concentration detection, which are crucial for diagnosing certain diseases. This review explores the recent advancements in Machine learning (ML) enhanced biosensors, highlighting their applications in health monitoring, disease diagnosis, and treatment evaluation. The novelty of this review lies in its comprehensive examination of how ML algorithms enhance biosensor technology by efficiently processing complex data and extracting actionable insights. Various types of biosensors, including electrochemical, optical, microfluidic, and wearable devices with the physiological signals they collect were introduced. The review empasize the role of supervised, unsupervised, and deep learning algorithms in improving biosensor performance, including tasks like classification, regression, clustering, and feature extraction. Despite significant progress, challenges remain in areas such as data privacy, ethical considerations, real-time data processing, computational requirement, and biosensor fabrication. Future prospects include the development of multifunctional biosensors, adaptive learning systems, and explainable artificial intelligence (AI) models to ensure the reliable, scalable, and ethical deployment in healthcare. Overall, this review underscores the potential of ML-reinforced biosensors to revolutionize diagnostics and personalized medicine while addressing key challenges that hinder their practical implementation.