Medical electrical devices play a critical role in modern healthcare, supporting diagnosis, therapy, monitoring, and personalised treatment. However, the complexity of these devices, together with the growing use of non-stationary electrical signals, bioimpedance-dependent parameters, and AI-assisted analysis, poses new metrological challenges in traceability, calibration, uncertainty evaluation, and regulatory compliance. Moreover, Artificial intelligence (AI) is increasingly used to extract quantitative information from medical images and to support diagnosis, prognosis, treatment planning, and response assessment. However, clinical translation remains limited by insufficiently characterised measurement uncertainty, variability across scanners and acquisition protocols, dataset shift, limited external validation, and poor reproducibility of computational biomarkers.

This Special Session addresses these metrological challenges by soliciting contributions on traceable measurement systems, calibration and verification procedures, measurement-uncertainty evaluation, and validated good-practice guidelines for key classes of medical electrical instrumentation. These will include defibrillators and electrosurgical analysers, electrocardiogram (ECG) and electroencephalogram (EEG) simulators, and bioimpedance measurement systems.

This Special Session aims to bring together researchers in measurement science, biomedical engineering, medical imaging, oncology, and trustworthy AI to discuss metrological principles and practical methods for developing reliable image-based AI systems. Particular attention will be given to the definition of measurable quantities, reference standards and ground truth, and uncertainty propagation.

The objective is to create an interdisciplinary forum addressing metrological challenges across medical technologies, from electrical medical devices to AI enabled medical imaging, with particular emphasis on measurement traceability, uncertainty quantification, robustness, validation, reproducibility, and clinical translation.

Topics of interest include, but are not limited to:
• Traceable calibration and performance evaluation of defibrillator analysers and electrosurgical-unit analysers;
• Reference measurement systems and traceable characterisation of electrocardiogram (ECG) and electroencephalogram (EEG) simulators;
• Metrological characterisation and time-domain measurement of low-level ECG and EEG waveforms;
• High-voltage and high-current impulse measurements;
• Broadband and high-frequency power measurements;
• Traceability and uncertainty evaluation for bioimpedance analysers over extended frequency and impedance ranges;
• Calibration methods in the complex impedance plane, including the effects of contact impedance and electrode–interface interactions;
• Reference materials, phantoms, equivalent circuits, and empirical models for tissue and body impedance;
• Impedance spectroscopy and tomography for biomedical applications, including their metrological validation;
• Artificial intelligence and machine-learning methods for waveform analysis, parameter estimation, correction, compensation, and uncertainty evaluation;
• Open-source tools, reference datasets, and software for medical metrology;
• Metrological requirements arising from medical-device regulation, standardisation, and clinical implementation.
• Metrological evaluation and validation of AI/ML methods in medical imaging
• Uncertainty quantification and propagation in AI-assisted clinical decision-making
• Robustness, reproducibility, and generalisability of medical imaging AI across devices, centres, and patient populations
• Reference datasets, ground truth, performance metrics, and traceability for AI-based medical imaging
• Regulatory, standardisation, and clinical-translation requirements for trustworthy AI in healthcare