Overview
TOGAI advances AI research to tackle global challenges in equitable healthcare. We focus on high-impact, scientifically demanding problems caused by a triad of structural constraints: shortage of specialists, limited diagnostics, and underdeveloped healthcare infrastructure, where AI’s potential is truly transformational.
By working in low-data regimes, scaling clinical expertise, and making our datasets open and adaptable, we enable and inform stronger healthcare delivery systems in similarly-resourced environments worldwide.
Our priority areas including ultrasound, X-rays, smartphone-based diagnostics, bioinformatics, EEG, and ECG.
Technical Focus
- Data-efficient learning: Semi-supervised, self-supervised, few-shot, and active learning methods.
- Robustness to distributional shifts: Domain adaptation, continual learning, and generalization across unseen clinical settings.
- Adaptation of foundation models: Re-tuning pre-trained models for low-data environments.
Our scope of research supports maternal health, rural obstetric ultrasound, and AI-powered task shifting locally in low- and middle-income countries, informed by direct collaboration with frontline health workers and field partners.
Team
Research Scientist
Research Associate
Research Assistants
Research Interns
- Prekshya Dawadi
- Angelina Ghimire
- Pranjal Khadka
- Bisram Acharya