Hybrid physics-informed machine learning and nanobiosensing strategies for precision liver cancer diagnostics

Liver cancer, particularly hepatocellular carcinoma (HCC), is a significant global health concern due to its asymptomatic early stages, biological diversity, and frequent late diagnoses that hinder effective treatment and survival rates. Traditional diagnostic methods, such as serum biomarker assays and imaging techniques, often lack the necessary sensitivity and specificity and highlight the urgent need for innovative, non-invasive diagnostic alternatives. This review emphasizes the potential of combining nanobiosensor technologies with physics-informed machine learning (PIML) to address these diagnostic challenges. Nanobiosensors utilize advanced materials like gold nanoparticles and graphene to achieve highly sensitive, real-time detection of HCC biomarkers, including alpha-fetoprotein (AFP) and non-coding RNAs, with detection limits reaching sub-nanomolar to femtomolar levels through various mechanisms. However, the clinical application of nanobiosensors is hindered by issues such as signal instability and environmental interference. PIML offers a solution by incorporating fundamental physical principles into machine learning models which is enhancing their predictive accuracy and robustness against data noise. This hybrid approach facilitates effective signal denoising, adaptive calibration, and the integration of multimodal data, thereby improving the overall diagnostic process. Main findings indicate that PIML-enhanced nanobiosensing systems significantly outperform traditional AI models in biomedical applications, demonstrating superior generalization and biologically relevant outputs even in the presence of limited data. The integration of these technologies creates a promising framework for advanced liver cancer diagnostics, enabling precise, non-invasive detection and personalized clinical decision-making. In conclusion, the convergence of nanobiosensors and PIML holds the potential to revolutionize liver cancer diagnostics, offering improved early detection and dynamic monitoring. However, to realize this potential, ongoing challenges related to computational scalability, sensor reproducibility, and regulatory validation must be systematically addressed through collaborative interdisciplinary efforts.