The integration of machine learning with biosensor technologies is transforming modern healthcare by enabling intelligent, real-time, and highly sensitive diagnostic systems. Traditional biosensors are often limited by noise sensitivity, data complexity, and lack of adaptability, whereas machine learning techniques offer powerful tools for signal processing, pattern recognition, and predictive analysis. This synergy is particularly important in applications such as disease detection, wearable health monitoring, environmental sensing, and point-of-care diagnostics.

This Topical Collection aims to provide the research community with standardized, reproducible, and innovative methodologies for designing, implementing, and evaluating machine learning–enhanced biosensors. It will cover experimental protocols, data acquisition techniques, model development workflows, and validation strategies across interdisciplinary domains, including biomedical engineering, data science, and nanotechnology. The collection will also highlight best practices for integrating AI models with biosensing platforms, ensuring robustness, scalability, and clinical relevance.

By bringing together contributions from experts in both biosensing and artificial intelligence, this collection will serve as a valuable resource for researchers seeking to develop next-generation smart diagnostic systems. It will support the advancement of precision medicine, improve healthcare accessibility, and foster innovation in intelligent sensing technologies.