In silico medicine utilizes advanced computational models to emulate human anatomy and disease processes, enabling safer and faster development of medical devices and drugs. Alongside, machine intelligence technologies like deep learning and generative AI significantly improve the analysis of complex biomedical signals, providing accurate, real-time, and clinically relevant insights. Together, these innovations foster a shift toward integrated, data-driven biomedical research and healthcare.

Through a combination of hands-on exercises and guided tutorials, attendees will build and refine a high-fidelity cardiac electrophysiology model, simulate pathological conditions such as regional ischemia, and apply deep learning approaches (e.g., LSTMs, CNNs) for automated detection of cardiac anomalies. Attendees will learn about Verification and Validation (V&V) of AI models to ensure the models behave reliably, safely, and transparently.

Highlights:

• Build and refine high-fidelity simulation models of human physiological systems, with a specific focus on cardiac electrophysiology.
• Learn to parameterize models for simulating a variety of diseased states – e.g., generate electrocardiogram (ECG) and cardiac electrogram (EGM) by simulating regional ischemia.
• Classification of ECG signals using time-frequency-based AI models and GenAI approaches. Verification of AI models for reliable, safe, and robust AI models.
• Learn FDA certification guidelines pertinent to In Silico models and AI-enabled medical devices.

Workshop presenters:

• Reza Fazel-Rezai
• Pourash Patel
• Garima Sharma

Continuous health tracking is advancing rapidly. Smartwatches now capture key physiological signals non-invasively, and continuous glucose monitors (CGMs) have transformed diabetes care. Yet many high-impact conditions (i.e., diabetes complications, cardiovascular disease, sleep apnea, and etc.) require monitoring condition-specific (bio)markers beyond traditional biosignals. Continuous measurement of these modalities can enable earlier detection of deterioration, real-time therapy guidance, and more personalized clinical decision-making; ultimately reducing complications, hospital visits, and overall burden on patients and health systems.

At the same time, emerging wearable platforms are generating rich longitudinal, high-frequency time-series datasets. Unlocking their value requires reliable analytics and predictive algorithms, similar to the rapid growth of data-driven approaches enabled by large-scale CGM data. This workshop will integrate perspectives from leaders in wearable physiological and neural sensing with clinical-grade signal processing, wireless sensing and edge-AI/communication systems for wearables, molecular sensing technologies, and data-driven modeling for prediction, personalization, and closed-loop health management. By bridging sensing innovation with algorithm development, the workshop will highlight how next-generation wearables can produce previously inaccessible health trajectories and how computational methods can translate these trajectories into actionable, predictive insights for health and disease management.

Workshop presenters:

• Omer T. Inan
• Netz Arroyo
• Daniel McDuff
• Ahmed A. Metwally
• Shalini Prasad
• Yao Qin

Gas embolism is a potentially life-threatening condition triggered by gas bubbles entry into the bloodstream. Invasive medical interventions or abrupt reductions in ambient pressure can result in intravascular gas embolism. The accumulation of gas bubbles initiates a cascade of pathophysiological phenomena progressing from platelet activation to ischemia and neurological dysfunction. This workshop integrates current knowledge of the biophysical mechanisms of bubble nucleation, progression, and vascular occlusion into a framework aligned with the adverse physiological consequences on circulation. The workshop further addresses the present state of clinical practice, diagnostic approaches, and therapeutic interventions. Initial studies on gas embolism utilized in vivo models, and recent in vitro and in silico platforms have provided reproducible and cost-efficient experimental approaches. The initial symptoms of gas embolism often overlap with stroke, myocardial infarction, or sepsis. Reliable detection of intravascular gas bubbles is constrained by the sensitivity, resolution, and accessibility of existing imaging modalities, particularly in systemic cases. Current treatment frameworks emphasize hyperbaric oxygen therapy, while adjunct pharmacological strategies to improve clinical outcomes are under investigation. The challenges responsible for the persistent neglect of gas embolism in both clinical and academic contexts are discussed, and a forward-looking perspective on strategies to overcome these barriers is presented.

Workshop presenters:

• Dan V. Nicolau
• David M. Eckermann
• Dan V. Nicolau Jr
• Richard Edward Moon
• Neal William Pollock
• Daniel Popa

This workshop brings together psychiatrists, clinical researchers, and machine learning experts to explore practical and translational applications of artificial intelligence for psychiatric assessment, monitoring, and intervention. The workshop emphasizes clinically grounded AI approaches that are designed to integrate seamlessly into real-world psychiatric practice. Through a series of focused presentations, speakers will demonstrate how personalized, agentic chatbots can be combined with wearable and mobile sensor data to support patient engagement and longitudinal assessment; how multimodal and translational AI methods can be developed to assess multiple mental and physical health outcomes; and how to design end-to-end AI workflows that address the unique challenges of psychiatric research, including heterogeneous data, missing modalities, and ethical deployment. A central goal of the workshop is to foster meaningful dialogue between clinicians and AI researchers, enabling shared understanding of clinical needs, modeling constraints, and validation standards that accelerate translation from research to practice. The workshop concludes with a hands-on session in which participants follow an interactive notebook demonstrating a real-world AI application in psychiatry, providing practical exposure to data processing, model development, and interpretation. This interactive component is intended to demystify AI tools and empower participants to apply these methods within their own clinical or research contexts.

Workshop presenters:

• Ervin Sejdic
• Amir Rahmani
• Matthew Flathers, Andrew Byun
• Mai Ali
• Christopher Lucasius

Brain–computer interfaces (BCIs) and related neurotechnologies are rapidly advancing, offering new ways to measure, analyze, and influence brain activity. Despite growing interest, the diversity of available tools and methods can make it challenging for researchers and clinicians to understand their capabilities, applications, and limitations.

This workshop provides a comprehensive overview of modern brain–computer interface (BCI) and neurotechnology tools, spanning signal acquisition, biomarker analysis, and clinical application. The session will introduce the BCI technology landscape, including low- and high-density EEG systems, invasive and non-invasive neural recordings, and multimodal approaches.

By the end of the workshop, attendees will gain a practical understanding of the current BCI landscape, key applications, and emerging directions, equipping them to explore new research opportunities or clinical implementations.

Workshop presenters:

• Micah Ching, BSc.
• Dean J. Krusienski, Ph.D.
• Nuri F. Ince, Ph.D.
• Kei Masani, Ph.D.
• Katrin Mayr MSc

The adoption of wearable devices is profoundly impacting on numerous healthcare sectors, ranging from rehabilitation and assistive technology to vital-sign monitoring and point-of-care diagnostics. Commercial products have been demonstrated to be capable of collecting biopotentials and physiological markers, with medical-grade signal quality. However, they are typically closed stacks, which limits the possibility of accessing and modifying the proprietary hardware and software. Conversely, the open-source ecosystem has reached a sufficient level of maturity to encompass the entire pipeline, from sensing hardware to edge intelligence. However, the landscape remains somewhat fragmented and navigating it without concrete examples and reusable workflows can be challenging.

This workshop highlights how open-source solutions can support the full end-to-end workflow of a healthcare wearable device design. Starting from customizable acquisition platforms for biopotentials (ExG) and wearable ultrasound in various form-factors, we then move to standardized software workflows for myoelectric control, showing how open analysis and benchmarking practices reduce engineering overhead and accelerate iteration. To broaden the perspective on advanced edge deployments, we include a neuromorphic open-source EMG viewpoint focused on ultra-low-power, low-latency inference. Finally, we introduce a device-agnostic integration layer that unifies heterogeneous devices through modular Python connectors for real-time streaming, visualization, and rapid experimentation.

Workshop presenters:

• Simone Benatti
• Tobias Röddiger
• Andrea Cossettini
• Erik Scheme
• Elisa Donati
• Mattia Orlandi

The increasing accessibility of mobile electroencephalography (EEG), together with wearable biometric devices (e.g. electrocardiography (ECG), electromyography (EMG), and respiration), have made repeated at-home recordings feasible in generating large, longitudinal, and multi-modal data. Such large-scale data is a key enabler for personalized “digital twins” to address the inter-individual variabilities in neural and physiological dynamics to improve health monitoring and disease research. Following this paradigm, mechanistic brain modeling (mean-field, neural mass, biophysical, and whole-brain models) is an emerging tool for EEG analysis: Latent model parameters can serve as subject-specific descriptors of neural dynamics or be analyzed in tandem with additional biometrics through machine learning models. Furthermore, simulations of brain models enable systematic testing of perturbations to explore personalized interventions.

This workshop presents recent theory and applications of mean-field and neural mass models, alongside machine learning approaches for fitting these models to EEG data, as well as novel personalized machine learning methods to leverage wearables in predicting clinical outcomes. It also includes hands-on tutorials on collecting and interpreting mobile EEG in combination with multimodal biometrics (ECG, EMG, and breathing) and using the collected data during the workshop to generate and simulate whole-brain models.

Workshop presenters:

• John David Griffiths
• Ervin Sejdic
• Heng Kang Yao
• Minarose Ismail

Data-driven deep learning is now widely used in medical imaging, yet purely data-driven models can become unreliable when measurements are noisy, acquisition settings differ from training (domain shift), or training data are dominated by simulations rather than diverse clinical examples. Physics-inspired and physics-informed learning addresses these challenges by embedding imaging physics and known signal structure into model design and training, improving robustness, reducing reliance on broad training-data diversity, and often enhancing interpretability.
This workshop introduces key concepts and practical, hands-on techniques for integrating physics into deep models for medical imaging. We will cover a broad spectrum of approaches, including known-operator and model-based layers, fusion of physics-derived features, deep supervision using intermediate physical quantities, physics-based regularization, and physics-informed loss formulations. Participants will learn when to use each strategy, how to implement them in modern architectures, and how these choices impact stability and generalization under real-world variability. Attendees will leave with actionable design patterns and implementation guidance for building imaging pipelines that transfer reliably across domains.

Workshop presenters:

• Ali Kafaei Zad Tehrani
• Peng Guo
• Ion Candel

The development of image-guided medical robotics is transforming the landscape of healthcare today. These tools offer several clinical advantages, such as dexterous manipulation of surgical instruments, surgical task automation, accurate digital patient models, and intraoperative tissue characterization. However, creating such systems requires the integration of AI, robots, imaging devices, and tracking equipment. This integration is a challenging barrier to entry for researchers to overcome before they can even begin innovating. Furthermore, many medical robotic systems rely on custom APIs or expensive research licenses that make them inaccessible.

Recently, several open-source platforms have gained momentum to facilitate the integration of various software and hardware components into a single system. These platforms enable modular and component-based development for rapid prototyping, reducing the initial startup burden. These tools can also incorporate dynamic robot simulation, allowing for a seamless transition between the virtual and physical environments for testing, which is crucial for the development of intelligent and AI-driven medical robotics.

This workshop will introduce participants to the landscape of image-guided robotics while providing a practical hands-on tutorial and demonstration. Our aim is for participants to leave with an understanding of how to begin prototyping medical robotic systems tailored to their own clinical use cases.

Workshop presenters:

• Pedro Moreira
• Laura Connolly
• Michelle Song
• Kaito Hara-Lee
• Lueder A. Kahrs

This workshop aims to provide participants with opportunities for hands-on physiological biosignal acquisition and interpretation with specific focus on Electromyography (EMG)/Photoplethysmography (PPG), Electrocardiography (ECG), and Electroencephalography (EEG). These experiences were designed for translation of physiological concepts to practical data, which is challenging to teach, allowsing student understanding of signal variation over time (accessing biosignals to infer physiological response).

Participants will engage in interactive sessions that combine theory with hands-on data collection using portable biosignal acquisition systems. The workshop will begin with a brief overview of the learning outcomes: i) to understand the principles behind ECG/PPG, EEG, and EMG; ii) to gain appreciation of sensor placement, signal recording techniques, and data acquisition; iiii) to perform basic signal processing and biosignal interpretation using open-source tools; and iv) translate relevant biosignals to cardiac/respiratory, neural, and skeletal muscle physiology.

Activities include:

• ECG/PPG: Capture heart rhythms, identify key waveform components, capture physiologic responses using easy to implement interventions and discuss clinical relevance.
• EEG: Measure brainwave patterns during rest and simple cognitive tasks, exploring artifact removal and basic interpretation.
• EMG: Record muscle activity during peripheral stimulation of involuntary skeletal muscle contractions to analyze signal amplitude, frequency, and latency.

Workshop presenters:

• Chris Bouwmeester
• Lindsey Fiddes
• Dawn Kilkenny

Smart monitoring represents an emerging paradigm for patient diagnosis, follow-up, and rehabilitation. This approach is becoming increasingly relevant as demographic trends indicate a growing number of individuals requiring care, alongside a declining availability of caregivers.
Imagine an environment in which every step taken and every surface touched can be seamlessly tracked. Within such a setting, patients can be monitored holistically, from eye movements to plantar pressure, while they move and interact in an ecological, real-life context.

This workshop explores how sensor networks integrated with artificial intelligence and machine learning algorithms can effectively support clinicians in delivering personalised and efficient care for neurological patients. Results from different clinical trials will be presented and discussed, enabling participants to reflect on the correlations between standard clinical assessments and sensor-derived metrics. The workshop will also encourage brainstorming on new application scenarios and foster interdisciplinary networking among professionals from different fields, including clinicians, occupational therapists, and engineers.

Through three representative use cases: multiple sclerosis, elderly populations, and pediatric patients, and contributions from invited speakers, the workshop will highlight the potential of smart monitoring technologies to enhance assessment, continuous monitoring, and rehabilitation outcomes.

Workshop presenters:

• Giulia Bodo
• Andrea Piccardo
• Megan O’Brien
• Joachim Hermsdörfer

Regenerative medicine aspires to restore, replace, or regenerate damaged tissues and organs, offering transformative solutions for conditions that today remain incurable or poorly treated. By harnessing cells, biomaterials, and bioactive cues, regenerative strategies aim to enable the body to heal itself, moving beyond symptom management toward true functional recovery and long-term tissue restoration.
Nowadays, regenerative medicine is entering a new phase in which biological innovation must be complemented by smart therapeutic and diagnostic systems capable of monitoring, controlling, and adapting regeneration in real time. This workshop proposes a first strategic bridge between the IEEE Engineering in Medicine and Biology Society (EMBS), and the Tissue Engineering and Regenerative Medicine International Society (TERMIS).

By bringing together leading researchers from both communities, the workshop will explore how sensing, imaging, energy-based therapies, data-driven approaches and other technologies can enable next-generation regenerative treatments. Topics will span regenerative therapies, real-time tissue monitoring, advanced in vitro systems, complex tissue fabrication technologies, modeling and translational platforms.
This workshop is conceived as a pilot initiative to foster long-term collaboration between EMBS and TERMIS, stimulating interdisciplinary research, shared roadmaps, and future joint activities. The session will consist of six invited talks followed by a moderated discussion engaging the EMBC community.

Workshop presenters:

• Leonardo Ricotti
• Paola Saccomandi
• Milica Radisic
• Stuart G. Campbell
• Anthony Atala
• Ellen Kuhl

While in most existing communication systems information is exchanged via electromagnetic (EM) waves, in molecular communication (MC) systems, information is encoded into the properties of molecules. MC is inspired by natural processes, e.g., quorum sensing or synaptic communication, where specific molecules are transmitted, processed, and received by biological entities. Operating where conventional communication concepts are not applicable, it is expected that synthetic MC systems allow for communication and signal processing at the nano- and microscale, thereby enabling communication between nano-machines and facilitating interactions with biological systems. A majority of research in MC is focused on healthcare applications, which are mostly envisioned to operate inside the human body. Healthcare applications address medical challenges including health monitoring, drug delivery, in-body communication, the spread of infectious diseases, and new technologies for medical implants and biosensors. The overarching vision of healthcare applications of synthetic MC is the realization of the Internet of Bio-NanoThings (IoBNT), which facilitates the autonomous detection and localized treatment of diseases through an in-body network of nanodevices and an external control unit. This workshop aims to provide a comprehensive introduction to synthetic MC and its potential to facilitate innovative and disruptive medical applications.

Workshop presenters:

• Maximilian Schäfer
• Andrew Eckford
• Werner Haselmayr
• Yifan Chen
• Ilangko Balasingham

Vibroacoustic sensing captures the vibrations and acoustic emissions generated during device–tissue interactions and converts them into rich guidance and characterisation information for minimally invasive and robotic procedures. Recent work has demonstrated that proximal vibroacoustic sensing can provide texture‑related cues for robotic palpation, enable layer and puncture detection in needle access, and support simple material and tissue differentiation in phantom and ex‑vivo studies without modifying the distal instrument tip. This half-day workshop will introduce the physical and signal‑processing foundations of vibroacoustic sensing, present state‑of‑the‑art use cases, and offer hands‑on experience with real‑time systems attached to endoscopic/robotic tools and needle‑like instruments. Participants will interact with live demonstrators for surface/texture exploration, aspiration and biopsy needle guidance, and pulsation sensing, and will analyse how characteristic signal signatures relate to underlying structures and events. A dedicated idea‑creation session will then identify new clinical and technological applications, opportunities for AI‑enabled decision support, and requirements for datasets and benchmarks. The workshop will conclude with the formulation of a roadmap and the initiation of a VIBROACOUSTIC research community to coordinate future collaborations, shared resources, and follow‑up activities within the IEEE EMBS ecosystem.

Workshop presenters:

• Michael Friebe
• Alfredo Illanes
• Katarzyna Marzec
• Nazila Esmaeili
• Katharina Steeg
• Sasan Matinfar