Authors - Sandhya Awate, Vipin Kumar Gupta Abstract - Rural communities face significant challenges in accessing essential healthcare services due to language barriers, limited health literacy, and insufficient medical support. Difficulties in understanding medical information, communicating symptoms, and interpreting diagnostic reports further hinder effective healthcare delivery. Additionally, unreliable internet connectivity restricts the reach of conventional digital health platforms. To address these challenges, this paper presents a Multilingual AI Health Assistant designed to operate on low-cost edge devices, enabling offline functionality to ensure continuous access and data privacy in low-connectivity areas. The proposed system integrates Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Optical Character Recognition (OCR), and speech recognition, allowing users to interact in their native languages via text or voice. It analyzes user-reported symptoms to predict probable health conditions, translates complex medical reports and prescriptions into simplified, localized explanations, and provides recommendations for nearby healthcare facilities. Unlike internetdependent telemedicine systems, this edge-based solution processes data directly on the device, safeguarding sensitive health information while maintaining reliability. By bridging linguistic and literacy gaps, the proposed assistant empowers rural populations with accessible and actionable healthcare insights, ultimately improving health outcomes in underserved regions.
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