This research investigates whether 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 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 investigates COVID-19 stigma among survivors in Nepal during the pandemic. It found that one-quarter experienced discrimination, social exclusion, and psychological distress. Misinformation, weak health-system preparedness, and lack of public trust fuelled stigma. The study argues that future pandemic preparedness must address social stigma alongside healthcare capacity.