This research uses artificial intelligence to predict the progression of Alzheimer’s disease and cancer using medical imaging data. By analyzing brain scans, tumor scans, and treatment responses, AI models can forecast disease development and treatment outcomes, enabling earlier intervention, more personalized care, and improved quality of life for aging populations.

This research improves photoacoustic imaging, a technique that uses light-generated sound waves to visualize tissue oxygenation deep inside the body. By calibrating measurements using highly oxygenated arterial blood, the method overcomes longstanding accuracy limitations and avoids skin-tone bias, potentially improving early tumor detection and non-invasive monitoring of tissue health.

This research redefines digital health literacy for an AI-driven world, emphasizing the alignment between users and technology. Using a Delphi method, it identifies three core components—knowledge, skills, and context. The resulting framework guides the design of digital health tools that better support behavior change by adapting to users’ real-world needs.

This research addresses the short lifespan of dental fillings by drawing inspiration from natural tooth structure. Using physics-based simulations, it designs materials with improved bonding and durability. The work has broader applications in aerospace, implants, and protective materials, demonstrating how bio-inspired engineering can enhance performance across multiple high-stress environments.

This research models blood flow in narrowed arteries and during catheterization using the Herschel–Bulkley fluid model. By simulating flow and drug dispersion, it identifies factors affecting unpredictability. These insights enable optimized treatments, improved medical device design, and better visualization for clinicians, ultimately enhancing safety and outcomes in cardiovascular care.

This research addresses excessive false alarms in hospital medical devices, which burden staff and distress patients. By detecting and filtering noisy data, the proposed system prevents false alerts while preserving true ones. Early results show complete removal of false alarms, improving efficiency, patient experience, and clinical response in healthcare settings.

This research develops realistic surgical simulation models using 3D printing to improve training for complex procedures. By enabling repeated practice in a safe environment, the models enhance skill, confidence, and performance. The work aims to make advanced surgical training more accessible while reducing errors and improving patient outcomes.

This research uses machine learning to predict trauma demand and optimise hospital scheduling. By forecasting patient volume and dynamically allocating operating rooms, it reduces cancellations, improves efficiency, and lowers costs. The system has the potential to transform healthcare delivery by balancing emergency and elective care more effectively.

Despite major advances in medicine, wound care has changed little in a century. This research explores how natural electrical signals in injured skin guide healing. By developing devices that mimic these signals, scientists aim to accelerate recovery and improve treatment for chronic wounds through bioelectric control of cellular behaviour.

This talk traces the devastation of the Black Death to highlight a modern crisis: antibiotic resistance. Misuse of antibiotics accelerates the rise of superbugs. Using AI and machine learning, the research identifies genetic resistance patterns and guides effective treatments, aiming to improve clinical decisions and prevent a return to a pre-antibiotic era.