TOGAI specializes in AI for intelligent and affordable health technologies in low-resource settings, tackling critical gaps in diagnostics, specialist access, and information equity. OGAI has achieved acceptances at international conferences, with two publications at MICCAI 2025 (including the MIRASOL satellite workshop) and one at ICFP 2025.

MICCAI focuses on medical image computing and computer-assisted intervention. TOGAI's paper has been accepted for oral presentation. This work represents PhD student Tanya Akumu at the University of Barcelona, mentored by Dr. Bishesh Khanal at TOGAI.

ICFP focuses on family planning and reproductive health research and practice. Out of over 5,000 abstracts submitted from more than 125 countries, TOGAI's paper was selected for the Poster Session category.

Conference Details

MICCAI 2025
23–27 September 2025 ( Daejeon Convention Center, South Korea)

ICFP 2025
3–6 November 2025 (Bogotá, Colombia)

Paper 1:

From Development to Deployment of AI-assisted Telehealth and Screening for vision- and hearing-threatening diseases in resource-constrained settings: Field Observations, Challenges and Way Forward

Mahesh Shakya, Bijay Adhikari, Nirsara Shrestha, Bipin Koirala, Arun Adhikari, Prasanta Poudyal, Luna Mathema, Sarbagya Buddhacharya, Bijay Khatri, Bishesh Khanal

MICCAI 2025: MIRASOL (Medical Image Computing in Resource-Constrained Settings & Knowledge Interchange Workshop)

Vision- and hearing-threatening diseases cause preventable disability in settings with few specialists and limited screening infrastructure. AI-assisted screening and telehealth can expand early detection, but deployment is challenging and few field experiences exist

This work shares lessons from developing scalable AI-assisted screening programs. Iterative co-design, interdisciplinary collaboration, prototyping, shadow deployments, and continuous feedback are critical for reducing usability hurdles. Similarly, public AI models and datasets are valuable despite domain shift, and automated image quality checks are essential for capturing gradable images in high-volume camps. By documenting these challenges, the work fills a gap in actionable field knowledge for real-world AI-assisted telehealth and mass-screening in resource-constrained settings.

Paper 2:

Adaptive Frame Selection for Gestational Age Estimation from Blind Sweep Fetal Ultrasound Videos

Tanya Akumu, Marawan Elbatel, Victor M. Campello, Richard Osuala, Carlos Martin-Isla, Ignacio Valenzuela, Xiaomeng Li, Bishesh Khanal, Karim Lekadir

MICCAI 2025

See GitHub Code

Blind sweep ultrasound is promising for prenatal care in low-resource settings, but current AI methods for gestational age estimation face key challenges: manual segmentation, inefficient frame processing, and suboptimal sampling with small datasets.

This work introduces SelectGA, a framework that adaptively selects the most informative and least redundant frames from ultrasound videos. Tested on data from diverse ultrasound devices, SelectGA reduced mean absolute error in gestational age prediction by 27%. The study shows that adaptive frame selection can make AI-powered ultrasound more accurate and computationally efficient, laying the foundation for sustainable prenatal care in resource-constrained healthcare systems.

Paper 3:

Can Chatbots Bridge SRH Information Gaps? A Community-Level Evaluation of ChatGPT in Nepal
Medha Sharma*, Supriya Khadka, Shilpa Lamichhane, Udit Chandra Aryal, Bishnu Hari Bhatta, Bijayan Bhattarai, Santosh, Bishesh Khanal
*Corresponding author: Medha Sharma

ICFP 2025

See full paper: Arxiv

This study evaluates ChatGPT-3.5’s performance in providing sexual and reproductive health (SRH) information to diverse community users across Nepal. Analyzing over 13,000 chatbot responses from students, community members, and Female Community Health Volunteers, experts assessed accuracy, usability, safety, and potential bias. Results showed that while 62% of responses were accurate, only 35% met full quality criteria, with gaps especially on anatomy and contraception topics. No significant demographic bias was found. The study highlights ChatGPT’s potential and limitations in low-resource, multilingual settings and offers key insights for developing safer, culturally relevant AI chatbots to improve equitable SRH access.

We congratulate the TOGAI team for their work, which continues NAAMII's commitment to leveraging AI for public health outcomes.

Three TOGAI Papers Accepted at MICCAI and ICFP