Transforming Global health with AI (TOGAI) aims to identify and solve difficult but important problems in global health where Artificial Intelligence (AI) can play an important role. We work on I) pushing the frontiers of AI with theoretical research relevant to health care ii) studying the implementation and adoption of AI in resource-constrained settings of LMICs in a responsible manner iii) research and innovate to push responsible AI adoption in healthcare. Our topics range from fundamental biological questions involving geometric deep learning for genomics and proteomics applied to diseases such as Tuberculosis (in collaboration with the Bioinformatics team) to low-cost smartphone-based diagnostic devices (in partnership with the computer vision team and other institutions such as KIAS). 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.
@article{wright2023fast, title={Fast fetal head compounding from multi-view 3D ultrasound}, author={Wright, Robert and Gomez, Alberto and Zimmer, Veronika A and Toussaint, Nicolas and Khanal, Bishesh and Matthew, Jacqueline and Skelton, Emily and Kainz, Bernhard and Rueckert, Daniel and Hajnal, Joseph V and others}, journal={Medical Image Analysis}, pages={102793}, year={2023}, publisher={Elsevier} }
@inproceedings{adhikari-etal-2022-covid, title = "{COVID}-19-related {N}epali Tweets Classification in a Low Resource Setting", author = "Adhikari, Rabin and Thapaliya, Safal and Basnet, Nirajan and Poudel, Samip and Shakya, Aman and Khanal, Bishesh", booktitle = "Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task", month = oct, year = "2022", address = "Gyeongju, Republic of Korea", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.smm4h-1.52", pages = "209--215", }
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