Research

ClarifAI is an LLM-based system designed to collect structured feedback in hard-to-reach environments such as busy clinical settings, where traditional feedback mechanisms fail. By embedding AI-powered clarification dialogues into clinical workflows, ClarifAI enables continuous, contextual improvement of decision-support tools at the point of care.

Privacy-preserving Large language models for Acronym Clinical Inference and Disambiguation. A framework for inferring and disambiguating clinical acronyms at scale — entirely on-premises — improving EHR readability and downstream NLP task performance without exposing patient data.

A Swift-based iOS application enabling clinicians to monitor real-time ambulatory data from pre- and post-operative patients. By surfacing patient vitals and trends directly on a clinician's device, MedWatch supports earlier intervention and improves continuity of care outside of hospital settings.

A drug-drug interaction decision support project in collaboration with the University of Utah and Vanderbilt University. DDInteract focuses on presenting complex pharmacological interaction data in clinically actionable ways, improving prescriber awareness and reducing adverse drug events.

Investigating the use of consumer-grade wearable devices to passively detect anxiety in clinicians and patients. By leveraging physiological signals such as heart rate variability and skin conductance, this project aims to provide real-time mental health insights without disrupting clinical workflows.

A user-centred mobile application delivering evidence-based clinical guidelines at the point of care, developed during my PhD in partnership with University Hospital North Midlands NHS Trust. Designed through iterative co-design with clinicians, resulting in 15 published usability recommendations across 5 papers.