The integration of AI-powered biosensors into healthcare systems has transformed the landscape of real-time physiological monitoring, offering unprecedented opportunities for personalized health management. These adaptive systems, driven by advanced machine learning algorithms, enable continuous monitoring and early detection of various diseases, paving the way for more efficient and targeted interventions. This chapter explores the development and application of AI-powered biosensors, focusing on key advancements in self-learning, sensor fusion, and low-power AI algorithms that contribute to real-time, energy-efficient health monitoring. The integration of deep learning for personalized biomarker analysis enhances the precision of disease prediction and offers promising prospects for precision medicine. Additionally, edge AI technologies enable real-time decision-making by processing sensor data locally, thereby ensuring timely interventions with minimal latency. Emphasis is placed on biocompatibility, energy efficiency, and the seamless integration of multimodal data to provide personalized, context-aware healthcare solutions. As AI continues to evolve, the future of biosensors holds immense potential for revolutionizing patient care, facilitating proactive disease management, and enabling a more tailored approach to healthcare delivery.ÂÂÂ
The integration of artificial intelligence (AI) with biosensors has significantly reshaped healthcare, enabling the development of systems that monitor physiological data in real-time with greater precision and efficiency [1]. AI-powered biosensors represent an innovative leap from traditional medical monitoring devices by introducing intelligent capabilities such as real-time decision-making, adaptive responses, and personalized health management [2]. These sensors, embedded with machine learning and deep learning algorithms, allow for continuous health monitoring, offering patients and healthcare providers real-time insights into various physiological parameters [3]. The continuous monitoring facilitated by these devices ensures early detection of potential health risks, allowing for timely intervention and more effective management of chronic conditions [4]. As the capabilities of AI expand, the scope of real-time monitoring continues to evolve, pushing the boundaries of what is possible in personalized healthcare [5].
A critical aspect of AI-powered biosensors is their ability to learn from the data they collect, adapting to an individual’s health profile over time [6]. Self-learning algorithms enable these devices to detect subtle changes in physiological data and predict potential health events before they manifest [7]. This adaptability is crucial for improving patient outcomes, as it empowers healthcare systems to move beyond reactive treatment strategies and transition to proactive care [8]. Machine learning techniques such as reinforcement learning and deep neural networks are employed to continuously refine these systems’ decision-making capabilities [9]. By using large-scale datasets, these algorithms can identify patterns that human clinicians may overlook, making them invaluable tools in the early detection and prevention of diseases such as diabetes, cardiovascular disease, and neurological disorders [10].
