Abstract:ASEAN countries face complex health crises driven by poverty, climate change, infectious diseases, natural disasters, and the withdrawal of external aid. The Thailand Myanmar border region, in particular, is challenged by a large influx of displaced people, the termination of external assistance, and reduced free medical services. These factors strain health systems and highlight the urgent need for innovative, resilient approaches. The border region is a hotspot for communicable diseases such as tuberculosis (including multidrug-resistant TB), malaria, HIV/AIDS, and vaccine-preventable diseases. The constant movement of refugees, migrant workers, and displaced people complicates disease control and access to essential health services. While public health responses have been robust in some areas, ongoing migration and instability in Myanmar continue to pressure local healthcare capacity. AI and digital technologies are increasingly recognized as transformative tools for improving access to health services in low- and middle-income countries, particularly in diagnostics, telemedicine, supply chain management, and epidemic forecasting. These technologies, when adapted to local contexts, can help overcome systemic barriers and improve health outcomes in resource-constrained settings. However, challenges remain, such as limited high-quality data for AI model training, weak infrastructure (electricity, connectivity), low AI literacy, and the deployment of “black-box” AI systems with low explainability and community trust. Human-centered design approaches actively involving end-users such as patients and frontline health workers are critical for effective and ethical deployment, especially among marginalized populations. The objective of this study is to identify issues surrounding access to diagnostics on the National Essential Diagnostics List for populations in crisis-prone areas, particularly at the Myanmar border in Thailand, and to explore how AI and other digital technologies could support the development of self-reliant systems for health. A mixed-methods approach will be employed, including based on a scoping literature review, key informant interviews and co-design processes with local stakeholders.
Keywords: Artificial Intelligence and Healthcare, AI for healthcare, digital health, migrant s and refugee health
