Transforming Global health with AI (TOGAI)

Transforming Global health with AI (TOGAI) is a research group within NAAMII led by Bishesh Khanal. This group focuses on theoretical and applied research in Machine learning (ML) that has the potential to transform global health. We closely collaborate with other themes within NAAMII and institutions outside NAAMII and explore a wide range of topics; all of these topics have a core machine learning component and global health applications. Our topics range from fundamental biological questions involving geometric deep learning for genomics and proteomics applied to diseases such as Tuberculosis (in collaboration with Bionformatics team) to low-cost smartphone-based diagnostic devices (in collaboration with computer vision team, and other institutions such as KIAS). We also strive to push the boundaries of ML theory such as semi-supervised learning and geometric deep learning etc. that have potential to tackle challenging problems in healthcare with a focus on applications that are directly relevant to Low-Income Countries (LICs). We believe that there are several unmet clinical needs of resource-constrained regions such as rural areas of Nepal that could benefit from the development of  advanced ML algorithms. We actively seek to find such problems by immersing and interacting in this environment. The advancement of the technology should prioritize devices or tools that will be low cost so that the solutions are accessible to the poor people who are unable to afford expensive health care costs. Priority areas: Ultrasound, X-rays, smart-phone, Bioinformatics, EEG, and ECG.