Reproducible Physics-Based Methods for Biomedical Sensing, Imaging and AI-Driven Diagnostics: From Functional Materials to Clinical Translation

Advanced functional materials, smart instrumentation, the Internet of Things (IoT) and artificial intelligence are reshaping biomedical sensing and diagnostics. Recent advances in MXenes, conductive hydrogels, carbon nanotubes and conductive polymers, combined with wearable and lab-on-chip sensors, networked instruments and data-driven analysis, are opening new routes from materials synthesis to clinical decision support.Yet progress across this chain remains fragmented. Differences in material characterisation, device calibration, data acquisition and computational workflows make results hard to compare across laboratories and slow their scale-up and clinical translation.The goal of this Research Topic is to bring together reproducible, physics-based methods that connect materials, devices, sensor networks and AI-assisted diagnostics, focusing on the physical principles, standardised protocols and validation strategies that make each step of this chain transparent and transferable.Specific objectives include:• Establishing characterisation practices for advanced functional materials that support cross-laboratory reproducibility• Clarifying the physical mechanisms that govern sensor–device–network coupling, from electrochemical and optical transduction to edge computing• Defining validation criteria for AI-based biomedical diagnostics, including clinical decision support and multimodal data integration• Translating integrated sensing and analysis pipelines into robust, field-deployable and clinically relevant toolsThis Research Topic welcomes contributions across medical physics, materials physics, device engineering, biomedical optics, computational physics, data science and the life sciences. Themes of interest include, but are not limited to:• Synthesis, characterisation and integration of MXenes, conductive hydrogels, carbon nanotubes and conductive polymers for sensing, energy and bioelectronics• Electrochemical, optical, plasmonic and wearable sensors; microfluidics and lab-on-chip platforms• Thermoelectric, piezoelectric and photovoltaic energy harvesting, and energy storage for self-powered devices• Sensor networks, data acquisition and transmission, and edge-computing solutions for biomedical instrumentation• Standardised pipelines for medical image analysis, multimodal data fusion and predictive modelling• Closed-loop systems that couple monitoring, analysis and feedback, such as neuromodulation and drug delivery• Documentation, quality assurance and benchmarking for complex multi-step experimental and computational workflowsWe invite Original Research, Methods, Review, Perspective, Hypothesis and Theory, and Technology and Code articles that present reproducible materials, devices, instruments, algorithms and biomedical applications. Authors are encouraged to describe experimental and computational methods in enough detail for others to reproduce their results, and may include supplementary videos of complex procedures. By bringing these together, the collection seeks to build shared standards across disciplines and accelerate the translation of next-generation sensing and diagnostic technologies. Topic editors: CH V V Ramana, Sabu Thomas, Md Hemayet Uddin, Thirupathi Rao N Source: https://www.frontiersin.org/research-topics/86640/reproducible-physics-based-methods-for-biomedical-sensing-imaging-and-ai-driven-diagnostics-from-functional-materials-to-clinical-translation