Recent Advances in Artificial Intelligence and Machine Learning Based Biosensing Technologies

Advancements in artificial intelligence (AI) and machine learning (ML) have transformed biosensing technologies, enhancing data acquisition, analysis, and interpretation in biomedical diagnostics. This chapter explores AI integration into biosensing, focusing on natural language processing (NLP), large language models (LLMs), data augmentation, and various learning paradigms. These technologies improve biosensor sensitivity, precision, and real-time adaptability. NLP automates biomedical text extraction, while LLMs facilitate complex decision-making using vast datasets. Data augmentation mitigates dataset limitations, strengthening ML model training and reducing overfitting. Supervised learning drives predictive models for disease detection, whereas unsupervised learning uncovers hidden biomarker patterns. Reinforcement learning optimizes sensor operations, calibration, and autonomous control in dynamic environments. The chapter discusses case studies, emerging trends, and challenges in AI-driven biosensing. AI’s convergence with edge computing and Internet of Things (IoT)-enabled biosensors enhances real-time data processing, reducing latency and expanding accessibility in resource-limited settings. Ethical concerns, including data privacy, model interpretability, and regulatory compliance, must be addressed for responsible AI applications in biosensing. Future research should focus on developing AI models resilient to bias, capable of continuous learning, and optimized for low-power, portable biosensors. Addressing these challenges will enable AI-powered biosensing to advance precision medicine and improve global healthcare outcomes. Through interdisciplinary approaches, AI and ML will continue to drive the evolution of next-generation diagnostic solutions.