Digital Health & Biomedical AI merges healthcare with information technology and artificial intelligence to create smarter, more connected health systems. Operating at the intersection of AI and medicine, this field is transforming the way healthcare is delivered, speeding up the development of treatments and helping to personalise medicine for individual patients. Our faculty’s strength in this area draws on biomedical engineers and computer scientists working together to harness big data, machine learning and mobile technologies for health benefits.
From hospital software that analyses patient data for early warning signs, to AI algorithms that assist in diagnosing medical images, digital health innovations make healthcare more efficient and proactive. Solutions like telemedicine and remote monitoring tools – backed by big data – are already reducing costs and improving access, outcomes and healthcare efficiency worldwide. Through user-friendly e-health platforms, data analytics, and telehealth services, we aim to support a future of healthcare that is predictive, preventive and patient-centric, extending quality care to everyone regardless of location.
Our research spans three strengths across multidisciplinary research
Our research aims to address the increasing demand for intelligent, data-driven healthcare systems. This research theme supports our broader mission to apply engineering innovation to global challenges, particularly in health and wellbeing. By integrating expertise in software engineering, data science, and systems design, we aim to enhance the delivery, accessibility, and efficiency of healthcare services.
This focus aligns with our strategic priorities of fostering interdisciplinary collaboration and equipping graduates with future-ready skills. The goal is to develop scalable digital health solutions that can support clinicians, improve patient outcomes, and enable more responsive and personalised models of care.
We are working on projects that apply artificial intelligence, machine learning, and software systems to healthcare contexts, such as clinical decision support, remote monitoring, and hybrid models of care. These efforts are designed to accelerate the translation of digital technologies into practical healthcare applications.
This research aims to improve digital health platforms and e-Health systems by applying AI, machine learning, and software engineering to challenges like clinical decision support and remote monitoring. These technologies enhance care delivery and accessibility, with real-world impacts such as faster diagnoses, better chronic disease management, and expanded access to telehealth for remote communities.
Professor Jinman Kim, Professor TJ Lim, Professor Kalina Yacef
Our research aims to unlock insights from complex medical and biological datasets using big data and artificial intelligence. This includes analysing genomic sequences, electronic health records, imaging scans, and wearable sensor data to identify patterns and make predictions that would be difficult or impossible for humans to discern unaided. The goal is to enable preventive and precision medicine, improve diagnosis, and support clinical decision-making. This theme aligns with our strategy to apply cutting-edge engineering and data science to global challenges, particularly in health and wellbeing.
We are developing algorithms and machine learning models that analyse vast datasets to predict disease outbreaks, identify patients at risk of complications, and improve diagnostic accuracy. For example, AI-driven tools are being used to detect subtle signs of disease in medical images and to combine data from multiple sources for more comprehensive assessments.
This research aims to improve biomedical data analytics with a focus on extracting actionable insights from complex health datasets, by developing AI and machine learning algorithms that analyse genomic data, medical images, and electronic health records. This enables earlier disease prediction, faster diagnosis, and more accurate treatment decisions, with broader impacts such as helping doctors detect complications sooner, supporting remote monitoring through wearables, and accelerating drug discovery for conditions like cancer and diabetes.
Professor Omid Kavehei, Professor Jinman Kim, Professor TJ Lim, Professor Kalina Yacef
Our research aims to overcome physical barriers in healthcare by developing digital tools that connect patients with providers regardless of location. This includes innovations such as video consultation platforms, remote monitoring devices, and telesurgery robotics. The goal is to make healthcare more accessible, especially for rural and remote communities, the elderly, and individuals with mobility challenges. This theme aligns with our strategy to create a digital, sustainable, and healthier future by applying engineering expertise to real-world challenges.
We are actively developing secure high-definition teleconferencing systems, wearable devices that transmit real-time health data, and AI-powered chatbots that assist patients in self-care and triage. These technologies are designed to maintain high-quality care while enabling virtual consultations and remote diagnostics.
This research aims to improve telemedicine technologies with a focus on bridging physical distance in healthcare delivery, by developing secure video consultation platforms, remote monitoring devices, and AI-powered virtual care tools. This enhances access to timely medical support and reduces hospital burden, with broader impacts such as enabling rural patients to consult specialists from home, helping elderly individuals manage chronic conditions remotely, and supporting virtual follow-ups after surgery.
Professor Jinman Kim, Professor Branka Vucetic, Associate Professor Tongliang Liu, Associate Professor Luping Zhou, Dr Mahyar Shirvanimoghaddam
