Smart Health Centre
Hero Carousel
Smart Health Centre
Who we are
The Smart Health Centre at the University of East London is a multidisciplinary hub where clinicians, data scientists, engineers, and public health specialists collaborate to turn complex health data into actionable insights. Our mission is to accelerate the translation of advanced analytics into routine care while ensuring the safeguarding of equity, privacy, and clinical integrity.
About the Centre
Our research integrates methodologies from machine learning (ML) and statistical modelling to build predictive systems that are not only technically robust but also clinically interpretable and ethically sound. These models are designed to assist healthcare professionals in anticipating complications, stratifying risk, and optimising care pathways, thereby contributing to more proactive, personalised, and efficient healthcare delivery.
Research focus
- Predictive analytics: Machine learning models that forecast deterioration, readmission, and long-term outcomes across cardiovascular, neurological, and metabolic disorders.
- Precision public health: Statistical and geospatial methods that map disease trajectories and identify underserved populations.
- Human-centred AI: Co-design with patients and staff to ensure transparency, usability, and ethical deployment of decision support tools.
- Edge & IoT for health: Secure, low-latency architectures that stream data from wearables and bedside devices into our analytics pipeline.
Our vision
The Smart Health Centre envisions a future where data-driven foresight is woven into every clinical interaction. We aim to set the global benchmark for transparent, rigorously validated, and clinically trusted models that:
- Detect illness earlier – surfacing real-time risk signals that move care upstream from reaction to prevention.
- Guide personalised treatment – translating genomics, imaging, and wearable streams into adaptive pathways tailored to each patient’s biology and context.
- Optimise resources – forecasting admissions, theatre utilisation, and workforce demand so health services can act before pressure builds.
- Ensure fairness and trust – embedding bias audits, explainability, and privacy by design so that benefits reach every community.
Through relentless innovation, interdisciplinary collaboration, and ethically grounded deployment, we will make predictive analytics a cornerstone of precision medicine and proactive, resilient healthcare worldwide.
Our mission
To catalyse a step change in global healthcare by translating cutting-edge data science into safe, equitable and actionable solutions. We:
- Deliver impact, not prototypes – driving algorithms from proof of concept to bedside implementation.
- Empower people and systems – equipping clinicians, patients and health leaders with intuitive, trustworthy decision support.
- Champion equity and privacy – embedding fairness, transparency and data protection at every stage.
- Grow future leaders – mentoring multidisciplinary talent and seeding innovation across the health workforce.
Funding and Grant Success
Since 2020, the Centre has secured multiple grants in competitive funding, including innovation and philanthropic awards from leading health foundations. Our active portfolio spans 12 grants and fellowships, supporting doctoral researchers, software engineers, and clinical innovators.
Strategic themes
Research: Predictive Modelling and Risk Stratification
The Smart Health Centre specialises in the development of advanced predictive modelling tools designed to support evidence-based clinical decision-making. Our work focuses on developing intelligent systems that predict patient outcomes, identify clinical risks, and inform treatment planning across a broad range of healthcare settings.
By harnessing the power of machine learning, statistical analytics, and real-world clinical data, we develop models that deliver accurate and timely predictions.
These tools help healthcare professionals identify patients at risk of complications, hospital readmissions, or poor recovery trajectories, ultimately contributing to more proactive and personalised care.
Our predictive modelling initiatives include:
- Forecasting post-surgical complications in orthopaedics and other surgical disciplines.
- Risk stratification for chronic conditions such as diabetes and cardiovascular disease.
- Modelling disease progression to inform early intervention and long-term care.
- Predicting treatment response to improve clinical outcomes and resource allocation
We work with large-scale datasets derived from electronic health records, medical imaging, clinical assessments, and biometric data, ensuring our models are both data-rich and clinically relevant. Emphasis is placed on model transparency, ethical integrity, and usability in real-world healthcare settings.
Through partnerships with NHS Trusts, academic institutions, and international collaborators, our predictive tools are being tested and implemented in clinical environments, bridging the gap between research and patient care.
Education: Shaping Future Innovators in Healthcare AI
SHC plays a crucial role in UEL’s postgraduate education programmes, particularly in healthcare analytics, computing, and digital education. By integrating advanced machine learning research into the academic curriculum, SHC is helping to train the next generation of healthcare innovators. Key educational contributions include:
- Postgraduate Programmes:
SHC contributes to UEL’s MSc and PhD programmes, offering students opportunities to specialise in healthcare AI and machine learning. Through these programmes, students engage in deep, hands-on research, focusing on applying machine learning techniques to real-world healthcare challenges. Topics of study include AI in medical data analysis, predictive modelling, and the application of machine learning (ML) in disease prevention. - Research Supervision:
SHC faculty members supervise MSc and PhD students, guiding them through independent research projects that often focus on applying machine learning in healthcare. These students have the opportunity to contribute to SHC’s ongoing projects, working with real-world datasets and learning how to build machine learning models that can address complex healthcare issues. - Internships and Collaborative Research:
SHC also offers internships and opportunities for students to collaborate on research projects with faculty members and external partners. Through these internships, students gain valuable experience working with cutting-edge AI tools and technologies, positioning them as leaders in the rapidly evolving field of healthcare AI.
Community Engagement: Promoting Inclusive and Equitable AI in Healthcare
The Smart Health Centre (SHC) at the University of East London is committed to advancing machine learning and digital health in ways that are inclusive and equitable, particularly for low-resource and underserved communities. Through international collaborations with academic and clinical partners, SHC co-develops AI technologies tailored to the specific
needs of diverse healthcare systems.
These partnerships focus on creating practical, context-aware solutions that align with local clinical workflows and infrastructure, while also fostering knowledge exchange and postgraduate research. By grounding innovation in real-world relevance and cultural sensitivity, SHC ensures that its technologies are accessible, impactful, and capable of reducing global disparities in healthcare access and outcomes.
Below is a snapshot of peer-reviewed work led or co-authored by faculty from the Smart Health Centre. This illustrative selection showcases our commitment to clinically grounded, ethically responsible AI innovation.
- AI in Infection Management – The Lancet Microbe, Oct 2024. Intersection of Artificial Intelligence, Microbes, and Bone and Joint Infections: explores AI-assisted diagnostics that accelerate early detection and guide targeted antimicrobial strategies.
- Predictive Modelling for Sports Injuries –. From Data to Decision: a literature synthesis of machine learning approaches that forecast injury risk, informing athlete management and load optimisation.
- AI in Orthopaedic Care – SICOT Paper, 2024. Bridging the Gap: maps adoption pathways for AI-enabled orthopaedic practice and identifies governance requirements for safe deployment.
- Literature Review on AI in Surgical Practice – Journal of ISAKOS, Nov 2023. Artificial Intelligence and the Orthopaedic Surgeon: surveys the current landscape of AI applications in diagnostics, surgical planning and intraoperative decision support.
- Clinician Perspectives on AI – Journal of ISAKOS, Jun 2023. FAIMS Study: captures healthcare professionals’ perceptions of AI usability, ethical considerations and implementation barriers.
- Machine Learning for Tendinopathy – European Journal of Orthopaedic Surgery & Traumatology, 2024. Predictive Utility of Machine Learning in Diagnosing Tendinopathy
This is a curated overview, not an exhaustive catalogue. Our full publication list and impact metrics are available via the Centre’s open dashboard.
Impact Dashboard
Real-world benefit at a glance
(aggregated from pilot evaluations, updated quarterly)
- Thousands of patient episodes supported by SHC algorithms since 2021.
- Multiple provider sites, including NHS Trusts and international partners, are actively deploying our work.
- Multiple digital health spinouts catalysed through our innovation pipeline.
- Clinical upskilled via SHC workshops and micro-credential programs.
Meet the team
The Smart Health Centre is led by an accomplished team of clinical experts, data scientists, engineers, and academic leaders. Our multidisciplinary approach ensures that our research is scientifically rigorous, ethically grounded, and directly responsive to the needs of patients, practitioners, and policymakers.
Professor Dr Hassan Abdalla
Centre Director | Provost, University of East London
Professor Abdalla is currently the Provost of the University of East London. Before that, he served as Pro Vice-Chancellor for Learning and Teaching and the College of Technology and Innovation, as well as Executive Dean of the School of Architecture, Computing, and Engineering. He is an international authority in the field of Smart/Future Cities, Disruptive Technologies, Brain Computer Interface (BCI), and Autonomous Systems with over 25 years of experience in both academia and industry.
Professor Dr Mohamed Imam
Executive Medical Director | Consultant Orthopaedic and Upper Limb Surgeon |
Professor Imam is a visiting professor and consultant orthopaedic surgeon who specialises in upper limb surgery, sports injuries, and complex trauma. He is the chief investigator of leading national and international studies. His work is widely recognised, and he has published more than 150 peer-reviewed articles in top international medical journals, as well as written over 12 textbook chapters on upper limb injuries. Professor Imam has designed orthopaedic instruments, developed and published various techniques, and authored two books.
Professor Imam directs the Centre’s musculoskeletal innovation portfolio. A fellowship-trained specialist in shoulder, elbow, and complex trauma surgery, he spearheads national and international clinical studies that are reshaping operative practice and rehabilitation.
- Research leadership: Chief investigator on national and industry-sponsored trials.
- Scholarly output: 150+ peer-reviewed papers, 12 textbook chapters, and edited four refereed books.
- Innovation track record: Patents and published minimally invasive techniques now adopted worldwide.
- Professional service: Board or committee member of OrthoGlobe, BESS, the Royal Society of Medicine, SICOT, the Royal College of Surgeons of England, and ISAKOS.
- Sports medicine: Trusted adviser to professional athletes and clubs across the UK.
Dr Rawad Hammad
Executive Technical Director | Senior Lecturer in Computing and Artificial Intelligence
Dr Hammad is a Programme Leader for MSc Computing, Programme Leader for MSc Digital Education, Technology Enhanced Learning Research Group Co-Leader, and a Senior Lecturer in Computer Science and Digital Technologies at the University of East London. Dr. Hammad has extensive experience in software engineering, Technology-Enhanced Learning (TEL), Artificial Intelligence in Education, and Smart Health research and practice.
Rawad contributed to and led various international projects, published research articles, and has been involved in the program committees of several conferences, including EC-TEL, LAK, AIED, and BUSTECH. Moreover, Dr. Hammad is supervising postgraduate students, currently five PhD students in addition to a varied number of MSc students. Dr Hammad is an executive committee member of the International Society of Artificial Intelligence in Education (AIED) and a committee member of different research bodies, conferences, including EC-TEL, LAK, AI for Post-COVID Education, and Networks in Education.
In 2018, he received his PhD in Software Engineering from the University of the West of England (UWE). In 2010, he received his MSc in Cognitive Computing from the University of London's Goldsmiths. He led numerous international projects such as: TRANSFER and SmartTech, which include partnerships with different universities, research centres, governmental institutes from different countries, including Japan, Germany, Finland, and the Middle East. Before coming to the University of East London, Dr Hammad was a Senior Education Solutions Researcher/Analyst at King's College London and a Researcher at the Centre for Complex Cooperative Systems at the University of the West of England.
Ahmed Elgebaly, MD
Postgraduate Researcher
Dr Elgebaly is studying for a PhD at the University of East London. He is working on a project to develop predictive models for sports injuries in professional athletes. Before starting his PhD, Elgebaly earned his MBBcH degree from Al Azhar University in Egypt in 2018. He has published more than 30 peer-reviewed publications in well-reputed international
medical journals and co-authored two textbook chapters. His current research focus is to utilise AI applications in developing healthcare solutions, with particular focus on sports science and cognitive neuroscience.
Ahmad AlThaher, MD, MSc (Oxon)
Postgraduate Researcher
Ahmad Althaher is studying for a PhD at the University of East London, where his current work focuses on the application of artificial intelligence in developing predictive models for postoperative outcomes in orthopaedic surgery. Holds an MBBS from the Jordan University of Science and Technology and an MSc in Medical Statistics from the University of Oxford. Brings expertise in biostatistics, clinical modelling, and evidence synthesis, with a growing publication record in high-impact international medical journals. Research interests lie at the intersection of data science, surgical outcomes, and translational health technologies.
Strategic Partnerships, Global Collaboration, and Engagement
The Smart Health Centre (SHC) at the University of East London is committed to advancing global healthcare through innovation, research, and digital transformation. We actively seek to establish meaningful partnerships with institutions and organisations that share this vision.
Through strategic international collaborations, the Centre develops, validates, and deploys advanced digital health technologies that are both clinically impactful and globally relevant.
We welcome collaboration with:
- Universities and research institutions interested in joint research, faculty exchange, or co-supervision of postgraduate students in areas such as artificial intelligence, medical data science, digital health, and epidemiological modelling.
- Healthcare providers and NHS Trusts aiming to integrate AI-driven tools into clinical workflows, participate in multicenter studies, or validate predictive algorithms in real-world environments.
- Industry partners, including health-tech startups and established technology firms, focused on co-developing innovative digital health solutions, wearable systems, or decision-support platforms.
Policy organisations and public health bodies are looking to collaborate on the ethical, legal, and regulatory frameworks necessary for the safe and equitable deployment of AI technologies in healthcare.
The Centre maintains strong international engagement through strategic partnerships with leading academic institutions, healthcare systems, and research organisations, including:
- Ashford and St Peter’s Hospitals NHS Foundation Trust and the UK National Health Service (NHS)
- Hashemite University (Jordan)
- Al-Balqa’ Applied University (Jordan)
- Ain Shams University (Egypt)
Through these partnerships, the Centre seeks to:
- Co-create evidence-based, scalable AI tools.
- Participate in joint funding applications.
- Expand international research networks and facilitate the cross-border transfer of knowledge.
- Foster capacity building in digital health innovation and contribute to the development of context-sensitive AI applications tailored to diverse healthcare environments.
- Translate academic discoveries into clinical and societal impact, addressing universal healthcare challenges such as access disparities, variability in outcomes, and the need for efficient, intelligent, and sustainable health systems.
If your organisation is interested in collaborating with the Smart Health Centre, we encourage you to contact us to explore potential areas of synergy.
For partnership inquiries, please contact: Email: smarthealth@uel.ac.uk
Together, we can drive measurable impact in global healthcare through responsible innovation and interdisciplinary research.