This research challenges the long-standing assumption that brain regions causing no errors during awake brain surgery are functionally unimportant. By measuring subtle delays in speech rather than errors alone, it introduces causal parametric mapping, offering surgeons a more sensitive way to preserve language function and improve patient outcomes.

This research has developed a five-minute smartphone memory test that detects subtle cognitive changes associated with early Alzheimer's disease. The tool identified symptom-free individuals with underlying disease and predicted future cognitive decline, outperforming expensive brain scans while offering a simple, accessible, and affordable approach to early diagnosis.

This talk explores red light laser therapy as an accessible recovery approach for minor brain injury. Using a portable device applied to eight head areas, the research tracks fatigue, pain, attention, memory, brain activity and saliva markers. Early participants showed improved symptoms, suggesting promise for practical, inclusive brain health support.

This research investigated whether AI-guided handheld ultrasound can help diagnose deep vein thrombosis (DVT) in primary care. Through a systematic review, a clinical study involving 565 patients, and stakeholder interviews, the research found promising results but highlighted challenges involving image quality, accountability, and integration into NHS healthcare systems.

This 3MT® presentation describes how artificial intelligence can help non-specialist clinicians diagnose deep vein thrombosis using AI-guided handheld ultrasound devices. By enabling faster point-of-care diagnosis in GP surgeries, the project aims to reduce hospital referrals, improve accessibility for vulnerable patients, and help healthcare systems manage increasing clinical demand more efficiently.

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 challenges the one-size-fits-all approach to obesity by comparing childhood- and adult-onset cases. Through physiological testing before and after weight loss, it examines differences in inflammation, metabolism, and fitness. Findings aim to support personalised treatments, improving patient outcomes and reducing the broader healthcare burden associated with obesity.

This research examines the overlap between IBS and eating disorder–like behaviours, where conflicting dietary advice creates clinical uncertainty. By interviewing patients and providers, it identifies two distinct groups based on motivation for food restriction. The goal is to develop tools that improve nutrition counselling and support better, safer patient care.