We build AI systems, blending algorithmic precision with genomics to personalize the prevention and treatment of complex infectious diseases.
We evaluate how artificial intelligence and multi-modal datasets can accelerate clinical and public health choices. Rendering care more accurate, timely, and actionable.
We investigate the genetic diversity of the Mycobacterium tuberculosis complex, host–pathogen relationships and bacterial lineages linked to virulence, outbreaks and drug resistance. Our work helps translate sequencing-based resistance diagnostics into clinical care.
We study large language models in clinical practice: matching treatment to guidelines, evaluating assistants in randomised studies, generating patient-friendly discharge information, and checking clinical letters for inconsistencies and mistakes.
Our projects range from breath tests for tuberculosis to cytokine-production assays for non-tuberculous mycobacteria. We also support targeted next-generation sequencing implementation in countries with a high burden of tuberculosis.
Guiding treatment with current evidence — using generative AI to help clinicians and patients navigate medical guidelines.
GUIDE-AI is developing vendor-independent “guideline navigators” that use large language models to identify guideline-recommended treatments and explain those choices to patients in plain language.
Clinicians, scientists and students working across responsible clinical AI, infectious-disease diagnostics and tuberculosis.
We are always looking for curious people who want to work at the intersection of clinical AI, infectious diseases and patient care.