Physics-Informed and Explainable Artificial Intelligence for Nanomaterial-Based Biosensors: From Sensor Design and Signal Processing to Clinical Translation

This research resource explores the integration of physics-informed and explainable artificial intelligence (XAI) within nanomaterial-based biosensing platforms, focusing on modalities such as surface plasmon resonance (SPR), terahertz (THz) metasurfaces, electrochemical systems, field-effect transistors, and wearable biosensors. Based on a critical narrative review of the literature, the source paper highlights how AI supports forward surrogate modeling, inverse design, spectral interpretation, and drift calibration. However, because our analysis is strictly limited to the provided abstract, specific experimental datasets, proprietary model architectures, and quantitative clinical validation metrics are not established here.

The review emphasizes that predictive accuracy alone is insufficient for clinical translation; instead, robust systems require mechanistic constraints, uncertainty quantification, and device-aware validation to bridge the simulation-to-experiment gap. For M.Tech researchers, this framework provides a structured direction to design and evaluate machine learning models that respect physical boundary conditions rather than relying solely on black-box data fitting. By addressing fabrication tolerances and small-data validation, the methodology guides the development of interpretable signal processing pipelines for real-world biomedical applications.

Message: Hi FE, I need help with “Physics-Informed and Explainable Artificial Intelligence for Nanomaterial-Based Biosensors: From Sensor Design and Signal Processing to Clinical Translation” in “Electronics & Communication Engineering”