Arogyabodhini: An AI-Powered Multilingual Healthcare Assistant for Disease Prediction and Smart Medical Support
Keywords:
Artificial intelligence, Disease prediction, Machine learning, Multilingual speech recognition, Natural language processing, Smart healthcare servicesAbstract
Healthcare accessibility remains a major challenge, particularly in multilingual regions where patients often struggle to communicate their symptoms due to language barriers, limited medical knowledge, and restricted access to specialized healthcare services. Existing digital healthcare systems generally support a limited number of languages and lack an integrated platform for multilingual communication, intelligent symptom analysis, doctor recommendations, and medical report processing. To address these challenges, this article proposes Arogyabodhini, an AI-powered multilingual healthcare assistant that provides intelligent medical support through voice-enabled interaction, disease prediction, and smart healthcare services. The proposed system enables patients to describe their health conditions using voice or text in regional Indian languages, where speech input is converted into a professional language (English) using speech recognition and translation techniques to ensure standardized processing. Natural Language Processing (NLP) is employed to extract relevant symptoms from patient descriptions, which are then analyzed using a Decision Tree-based machine learning model to predict possible diseases. Furthermore, Arogyabodhini integrates a centralized doctor recommendation system that identifies suitable specialists based on predicted diseases, specialization, and availability while supporting online appointment booking, digital prescription management, and emergency alerts for critical health conditions. A secure and user-friendly dashboard enables patients to access their health records, consultation history, uploaded reports, and medical recommendations. By integrating Artificial Intelligence, Machine Learning, Natural Language Processing, speech recognition, and modern web technologies into a unified healthcare platform, the proposed system aims to improve healthcare accessibility, reduce communication barriers, support early disease identification, and simplify patient–doctor interaction, thereby contributing to the development of inclusive, intelligent, and technology-driven healthcare services, particularly for rural and linguistically diverse communities.
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