Physics-Informed and Explainable Artificial Intelligence for Nanomaterial-Based Biosensors: From Sensor Design and Signal Processing to Clinical Translation
- 1Qi S.r.l., Via Monte d’Oro 2/A, Pomezia, 00071 Roma, Italy;
Abstract
Artificial intelligence (AI) is increasingly used in biosensing, yet its role is often limited to post-processing or high-accuracy regression on simulation-generated datasets. This critical narrative review examines physics-informed and explainable AI for nanomaterial-based biosensors, emphasizing graphene and other two-dimensional materials, surface plasmon resonance (SPR), terahertz (THz) metasurfaces, electrochemical platforms, field-effect transistors, surface-enhanced Raman spectroscopy, and wearable systems. Recent peer-reviewed studies and relevant technical guidance were critically compared with respect to sensing physics, data provenance, AI task, interpretability, validation strategy, and evidence level. Across these modalities, AI supports forward surrogate modeling, inverse design, spectral interpretation, classification, calibration, and uncertainty-aware decision support, but predictive accuracy alone does not establish translational maturity. Particular attention is given to explainable AI, the distinction between physics-guided and genuinely physics-informed learning, small-data validation, fabrication tolerance, drift, and the simulation-to-experiment gap. Quantitative comparisons place reported SPR and THz sensitivities in context, while independent experimental studies provide benchmarks for real-device and biomedical validation. Clinically credible intelligent biosensors should combine mechanistic constraints, uncertainty quantification, device- and batch-aware validation, interpretable features, realistic biological matrices, and prospective evaluation.
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BibTeX
@article{bellucci2026physicsinformed,
title = {{Physics-Informed and Explainable Artificial Intelligence for Nanomaterial-Based Biosensors: From Sensor Design and Signal Processing to Clinical Translation}},
author = {Stefano Bellucci},
journal = {Bioengineering (Basel, Switzerland)},
year = {2026},
month = sep,
doi = {10.3390/bioengineering13101133},
url = {https://europepmc.org/article/PMC/PMC13701277},
note = {CC BY},
}
RIS
TY - JOUR
TI - Physics-Informed and Explainable Artificial Intelligence for Nanomaterial-Based Biosensors: From Sensor Design and Signal Processing to Clinical Translation
AU - Stefano Bellucci
PY - 2026
DA - 2026/09/01
JO - Bioengineering (Basel, Switzerland)
DO - 10.3390/bioengineering13101133
UR - https://europepmc.org/article/PMC/PMC13701277
AB - Artificial intelligence (AI) is increasingly used in biosensing, yet its role is often limited to post-processing or high-accuracy regression on simulation-generated datasets. This critical narrative review examines physics-informed and explainable AI for nanomaterial-based biosensors, emphasizing graphene and other two-dimensional materials, surface plasmon resonance (SPR), terahertz (THz) metasurfaces, electrochemical platforms, field-effect transistors, surface-enhanced Raman spectroscopy, and wearable systems. Recent peer-reviewed studies and relevant technical guidance were critically compared with respect to sensing physics, data provenance, AI task, interpretability, validation strategy, and evidence level. Across these modalities, AI supports forward surrogate modeling, inverse design, spectral interpretation, classification, calibration, and uncertainty-aware decision support, but predictive accuracy alone does not establish translational maturity. Particular attention is given to explainable AI, the distinction between physics-guided and genuinely physics-informed learning, small-data validation, fabrication tolerance, drift, and the simulation-to-experiment gap. Quantitative comparisons place reported SPR and THz sensitivities in context, while independent experimental studies provide benchmarks for real-device and biomedical validation. Clinically credible intelligent biosensors should combine mechanistic constraints, uncertainty quantification, device- and batch-aware validation, interpretable features, realistic biological matrices, and prospective evaluation.
SN - 2306-5354
ER -