Mpofu, Kelvin TTsebesebe, Nkgaphe TManoto, Sello TKarakuş, S2026-09-042026-09-042026-06978-1-80631-594-92631-5343DOI: 10.5772/intechopen.1016231http://hdl.handle.net/10204/14926Biosensing technologies are experiencing a major transformation driven by advances in artificial intelligence, quantum computing, and distributed digital infrastructures. Classical artificial intelligence (AI) has already demonstrated significant value in biosensing through improved signal processing, pattern recognition, and data-driven diagnostics. However, the increasing complexity of biosensor data, together with challenges such as limited datasets, noise, and real-time decision-making requirements, highlights the limitations of purely classical approaches. Quantum artificial intelligence (QAI), which combines quantum principles such as superposition, entanglement, and hybrid quantum–classical learning, offers a promising alternative by enabling improved feature extraction and enhanced performance in data-constrained environments. In parallel, blockchain technology introduces a decentralized and secure framework for managing biosensing data, supporting integrity, traceability, and trust in distributed diagnostic and monitoring systems. This chapter presents a comprehensive overview of classical AI, quantum AI, and blockchain technologies as applied to next-generation biosensing systems. It discusses their individual roles, synergies, and integration into unified architectures for secure, intelligent, and resilient biosensing. Applications in healthcare diagnostics, environmental monitoring, and point-of-care testing are highlighted, alongside key challenges, ethical considerations, and future research directions toward quantum-ready and trustworthy biosensing ecosystems. In addition, the chapter considers how these technologies may support the development of more adaptive biosensing platforms capable of learning from diverse data sources, operating across decentralized healthcare environments, and maintaining data security throughout the diagnostic workflow. Emphasis is placed on the importance of interdisciplinary design, where biosensor engineering, quantum machine learning, quantum information science, blockchain and biomedical development converge to address practical diagnostic challenges.FulltextenArtificial IntelligenceBiosensingMachine learningLarge language modelsNatural language processingBlockchainBook ChapterN/A