Innovative Pathways for Equitable Access to Medical Knowledge for maternal and child health care

Existing maternal and child healthcare challenges highlight the uneven distribution of information between mothers and healthcare professionals. While AI solutions can help bridge this gap by providing language-specific, culturally relevant healthcare information, many AI-based solutions originate from a predominantly Northern Hemisphere perspective, which limits their transferability, increases resource demands for adaptation and localization, and ultimately delays their impact across the Global South.

Current AI-based solutions in healthcare often overlook the language and cultural factors crucial for effective patient interactions. Evidence also indicates that a lack of language accessibility and cultural relevance can cause misdiagnoses and poorer maternal and child healthcare results. In this context, including local languages is vital for providing proper maternal and child healthcare services in the Global South.

By embracing open science principles, we propose adopting a decolonial approach, using Ethiopia as a case study, to develop an expandable AI-based solution for maternal and child healthcare services. This will be achieved through a co-creative process that integrates local language needs and cultural context, ensuring more accessible and effective healthcare interactions. We aim to bridge the gap between medical terminologies and local language comprehension, transforming healthcare dynamics and enhancing accessibility. Starting with Amharic, the goal is to create a cognitive assistant model that facilitates the exchange of medical information in Amharic. The next step will be to expand to other regional languages in Ethiopia and neighboring Sub-Saharan countries, thereby incorporating local languages into AI toolchains to promote equitable access to vital information.