This research develops patient-specific digital twins of the heart to improve radiofrequency ablation for cardiac arrhythmias. By simulating heat transfer, tissue damage, and electrical activity, these computational models could improve treatment accuracy, reduce repeat procedures, accelerate medical device development, and advance the future of personalised cardiovascular medicine.

This research investigates how the choice of time interval affects reinforcement learning models for sepsis treatment in intensive care. By comparing one-, two-, four-, and eight-hour patient timelines, it demonstrates that shorter intervals better capture patient dynamics, improve estimated survival outcomes, and may enable more effective AI-assisted clinical decision-making.

This research develops a brain-inspired optical imaging system that mimics human vision to reconstruct objects hidden by fog, smoke, and biological tissue. Combining event-based cameras, spiking neural networks, and neuromorphic processors, it enables fast, energy-efficient imaging with applications in autonomous vehicles, emergency response, and non-invasive medical diagnostics.

This research develops antibacterial nanostructured surfaces inspired by natural materials such as cicada wings. The engineered surfaces physically rupture bacteria using nanoscale needle-like structures, avoiding traditional antibiotics and reducing the likelihood of antibiotic resistance. The technology could improve infection control in medical devices, implants, and hospital environments.

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 introduces iCares, a smart wound-monitoring bandage designed to detect infection and inflammation before visible symptoms appear. Using biosensors, fluid sampling, and machine learning, the system provides real-time wound analysis, enabling earlier intervention, personalized treatment, reduced complications, and improved healing outcomes for patients with chronic wounds.

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 uses wearable data and AI to detect disease earlier by analyzing continuous health signals rather than isolated clinical snapshots. By personalizing models to individual baselines, the system identifies subtle changes linked to conditions like infections, heart issues, and mental health crises, enabling earlier intervention and potentially saving lives.