The mortality burden of head and neck cancer (HNC) is increasing globally and disproportionately affects people in low-and middle-income countries with limited medical workforce. To address this issue, artificial intelligence (AI) algorithms are increasingly being explored to process medical imaging data, demonstrating competitive performance. However, the clinical adoption of AI remains challenging as clinicians struggle to understand how complex AI works and trust it to use in practice. In addition, AI may not perform well on varying data qualities of endoscopy videos for HNC screening and diagnosis from multiple sites. In this project, our international and interdisciplinary team will collaborate with clinicians from multiple sites (e.g. Singapore, the U.S., and Bangladesh) to collect a diverse, multi-site dataset. In addition, we aim to design and develop computational techniques and practices to improve collaborations between clinicians and AI for the triage and diagnosis of HNC. Specifically, these techniques include a YOLOv5-based glottis detector, a classifier of patient's status using clinical endoscopy videos, uncertainty quantification techniques, and interactive Vision Language Model-based AI explanations, which will enable clinicians to understand AI outputs and provide their inputs to improve AI. After developing our system, we will evaluate the effectiveness of these computational techniques in enabling AI-assisted point-of-care triage and decision-support for HNC, particularly in resource-limited settings.