Medical Technology and Digital Health Research Group

The Medical Technology and Digital Health Research Group bridges engineering innovation and clinical care through needs-driven research. We develop and validate technologies that span wearable sensors, artificial intelligence, process mining, and digital health platforms.

Our multidisciplinary approach combines expertise from engineering, medicine, data science, and implementation science to address real healthcare challenges. We work at the intersection of fundamental technology development, clinical validation, and healthcare system implementation, ensuring our innovations translate from laboratory discoveries to practical applications that improve patient care.

Our work is deeply integrated with SMAILE (Stockholm Medical Artificial Intelligence and Learning Environments), Karolinska Institutet's core facility for AI in healthcare. This close relationship reflects our service-driven mindset; we continuously develop new methodologies, tools, and training programs that can be offered to the broader research community through SMAILE's infrastructure. This synergy accelerates innovation by making advanced capabilities accessible to researchers and clinicians across the institution.

Publications

All publications from group members

Staff and contact

Group leader

All members of the group

Alumni

  • Abdelakram Hafid
  • Sara Benouar
  • Pablo Perez Garcia

Research

Research Themes

Our AI research focuses on developing interpretable, trustworthy models for early disease detection and clinical decision support. Applications include ECG analysis for detecting heart failure, predictive models for disease progression in chronic conditions, mental health monitoring through objective measures, and risk stratification algorithms. We emphasize transparent AI that clinicians can understand and validate rather than black-box approaches.

As founding members of the Process-Oriented Data Science for Healthcare Alliance, we pioneer interactive process mining methodologies that reveal how healthcare processes actually occur by analyzing electronic health records and administrative data. This enables identification of bottlenecks, care pathway variations, and opportunities for quality improvement. We apply these methods to emergency department optimization, the analysis of chronic disease progression, and the transformation of value-based healthcare.

We design and validate comprehensive digital health platforms that enable remote patient monitoring and distributed care delivery. Our work encompasses the entire innovation pipeline, from user experience design through clinical validation to regulatory compliance and reimbursement models. Projects span cancer survivorship support, diabetes management, high-risk pregnancy monitoring, and mental health tracking.

In collaboration with Professor Malin Holzmann at Karolinska University Hospital's Department of Obstetrics and Gynecology, we develop algorithms for computerized cardiotocography (CTG) analysis during pregnancy and labor. This work addresses critical challenges in fetal monitoring by reducing observer variability and enabling earlier detection of fetal distress. Our algorithms quantify fetal heart rate deceleration patterns, analyze short-term variation, and identify signs of complications that might otherwise go unnoticed until delivery complications arise. 

We develop advanced wearable systems, including sensorized garments with textile electrodes for continuous monitoring of cardiac, respiratory, and body composition. Our work encompasses the development of fundamental sensors, signal processing for real-world data, and clinical applications in the management of chronic diseases. 

Key innovations include impedance cardiography systems, bioimpedance spectroscopy for assessing fluid status, and smart textiles that integrate medical-grade monitoring into comfortable, everyday garments.

Selection of Research Projects

LifeChamps
EU CORDIS: Project 875329

LifeChamps creates a collective intelligence platform combining wearable sensors, AI frailty assessment, and personalized recommendations to support older cancer survivors managing treatment side effects and chronic conditions. 

The system integrates activity monitoring, symptom tracking, and process mining analysis of care pathways to identify opportunities for intervention and improvement. Clinical validation across Sweden, Spain, and Scotland demonstrated 93% user retention among older adults, challenging assumptions that elderly populations struggle with digital health technologies. The platform enables early detection of declining function and provides actionable guidance for patients, families, and healthcare providers.

EIT Health FAITHFUL

FAITHFUL deploys artificial intelligence for early heart failure detection through ECG analysis in primary care settings. 

The project addresses the critical challenge that heart failure diagnosis typically occurs only after symptoms emerge, often years into disease progression. By analyzing routine ECGs with AI algorithms that detect subtle patterns invisible to human readers, FAITHFUL enables intervention before hospitalization becomes necessary. The 14-partner consortium spanning Sweden, Spain, France, and Estonia validates the approach across diverse healthcare systems while developing sustainable reimbursement models that incentivize prevention over reactive treatment.

EIT Health MoodMon

MoodMon develops wearable systems and AI algorithms for objective mental health assessment in chronic affective disorders, including bipolar disorder and major depression. 

The project addresses the limitation that current mental health monitoring relies almost entirely on subjective self-reports and infrequent clinical visits. By continuously analyzing physiological signals, voice characteristics, and behavioral patterns, MoodMon enables the early detection of mood episodes, providing clinicians with objective data that complements traditional assessments. The technology offers particular promise for preventing hospitalizations through timely intervention when early warning signs appear. We are providing Regulatory and AI expertise regarding the retraining of AI models as part of software as medical devices (SaMD).

VALUE 
EIT Health: VALUE Project

VALUE applies interactive process mining methodologies to healthcare system transformation, enabling data-driven quality improvement across emergency departments, chronic disease management, and surgical pathways. 

Unlike traditional process improvement approaches that rely on expert opinion about how care should be delivered, VALUE analyzes actual patient pathways through electronic health records to reveal bottlenecks, variations, and opportunities for optimization. The project demonstrated how revealing that elderly patients experienced longer emergency department wait times led to targeted process changes, thereby improving the quality of care. VALUE's methodologies are now being adopted by hospital quality improvement departments across Europe.

EIT Health SMARTDIABETES

SmartDiabetes offers comprehensive diabetes management programs tailored for older adults, incorporating remote monitoring, personalized care plans, and value-based reimbursement models. 

The project addresses the reality that traditional diabetes care focuses narrowly on glucose control, while ignoring broader health aspects, such as nutrition, physical activity, mental well-being, and medication management. By integrating these dimensions into holistic care supported by digital tools, SmartDiabetes demonstrates improved outcomes while reducing costs. The value-based reimbursement component, which pays for health outcomes rather than procedures, represents a potentially transformative innovation in chronic disease management.

EIT Health PregnaDigit EU

PregnaDigit EU revolutionizes high-risk pregnancy monitoring through remote digital patient care (RDPC) platforms, enabling home-based monitoring rather than requiring frequent hospital visits. 

The project validates safety, effectiveness, and cost-effectiveness across multiple European countries while developing implementation frameworks addressing organizational changes, reimbursement models, and regulatory requirements. By demonstrating RDPC's viability in pregnancy care, where safety concerns are paramount, the project provides a template for extending remote monitoring to other conditions, including heart failure, respiratory diseases, and post-operative recovery.

Integration and Connection

Clinical Integration at Karolinska University Hospital

The research group maintains strong operational ties with Karolinska University Hospital's Department of Medical Technology, ensuring direct pathways from research innovation to clinical implementation. 

Fernando Seoane's combined position as Research, Innovation, Development, and Education officer at the hospital's Medical Technology Department creates a vital bridge between academic research and practical healthcare delivery. This dual role enables the group to identify real clinical needs directly from hospital operations, test innovations in authentic healthcare settings, and facilitate rapid translation of validated technologies into routine clinical use. 

The connection ensures research priorities align with actual healthcare challenges while providing access to clinical expertise, patient populations for validation studies, and infrastructure for implementing new medical technologies across Karolinska University Hospital's comprehensive healthcare network.

Connection to SMAILE

The Medical Technology and Digital Health Research Group operates with a fundamentally service-driven mindset, viewing research not just as a means to generate publications but as a means to develop capabilities for the broader healthcare innovation community. This philosophy aligns perfectly with SMAILE's mission as Karolinska Institutet's core facility for AI in healthcare.

Our relationship with SMAILE is deeply synergistic. Research breakthroughs in our group directly enhance SMAILE's service offerings. The process mining methodologies we develop become tools available to clinical departments. Wearable sensor expertise informs consultations with medical device companies, and experience in implementing medical AI under regulatory frameworks guides other researchers through certification processes. Conversely, SMAILE's infrastructure enables our research by providing computational resources, technical support, and connections to clinical collaborators we couldn't access independently.

We continuously work to expand services and training programs deliverable through SMAILE. Current developments include new workshops on interactive process mining for clinical quality improvement, training modules on medical AI regulatory compliance under the European Union's AI Act, and consultation services for implementing wearable monitoring systems in clinical research. This ensures SMAILE remains at the forefront of healthcare innovation by rapidly translating research advances into accessible capabilities.

Our commitment extends beyond making SMAILE a service provider to positioning it as a genuine innovation accelerator. By developing methodologies, tools, and educational programs specifically designed for practical implementation rather than just academic demonstration, we help clinical researchers and healthcare organizations overcome technical barriers that would otherwise prevent the adoption of innovation. This service-driven approach, ”making advanced capabilities accessible, understandable, and implementable- defines our contribution to Karolinska Institutet's mission of improving human health through research and education.

Collaboration and Partnerships

Educational Partnerships with KTH Royal Institute of Technology

The group maintains extensive educational collaborations with the KTH Royal Institute of Technology, contributing to both undergraduate and graduate-level programs in medical technology and biomedical engineering. Several PhD students in the research group hold joint appointments between Karolinska Institutet and KTH, benefiting from exposure to rigorous engineering methodology alongside a clinical context. This creates graduates with rare, combined competencies that are highly sought after for both academic positions and industry roles in medical technology companies. 

Group members regularly teach courses that integrate engineering fundamentals with healthcare applications, supervise thesis projects addressing real clinical challenges, and coordinate joint seminars that bring together medical and engineering perspectives. This partnership exemplifies the interdisciplinary approach essential for modern healthcare innovation, ensuring the next generation of medical technologists masters both technical excellence and clinical relevance.

Collaboration Partners

Healthcare Institutions

Region Stockholm - Implementation across Stockholm's healthcare network serving 2+ million people
Karolinska University Hospital - Clinical validation and needs identification
Karolinska University Hospital, Medical Unit for Pregnancy and Delivery Care - Fetal monitoring research with Associated Professor Malin Holzmann
Academic Primary Health Care Centre - Primary care research and implementation
Vall d'Hebron University Hospital - (Spain), Multi-center clinical studies
University Medical Centre Utrecht - (Netherlands), Obstetric care innovation
Hospital 12th of October - (Spain), Cardiovascular research
Multiple European Regional Healthcare Systems - Cross-border validation studies

Universities and Research Institutes

University of Borås - Textile-electronic sensing for Wearable Biomedical instrumentation 
KTH Royal Institute of Technology - Engineering and sensor development
Erasmus University Rotterdam - Implementation science and health services research
Universidad Politécnica de Madrid - Health information technology
Karolinska Institutet - Medical research and clinical expertise
Process-Oriented Data Science for Healthcare Alliance - International process mining community