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 investigates taste alterations experienced by cancer patients during chemotherapy and radiotherapy. Using electrogustometry and flavour profile analysis, the study measures and categorizes changes in taste perception to guide the development of tailored food products that improve nutrition, comfort, and quality of life for people undergoing cancer treatment.

Infertility affects one in six adults and carries profound emotional, psychological, and social burdens often overlooked in medical care. This research evaluates a four-week yoga intervention for individuals undergoing IVF, aiming to reduce anxiety and depression while improving quality of life, addressing the unmet psychosocial needs of those experiencing infertility.

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.

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.