Jul 2026· 2026 7th International Conference on Smart Systems and Inventive Technology (ICSSIT)· pp. 451-458· 0 citations· 15 references
Abstract
Access to timely healthcare remains a critical challenge in rural India, where physician shortages, infrastructure deficits, and language diversity collectively hinder effective medical intervention. This paper presents SwasthyaVani (meaning Voice of Health), a voice-enabled multilingual clinical decision support system designed specifically for rural populations in India. The system accepts free-form symptom descriptions via speech or text in Telugu, Hindi, and English, processes them through a custom multilingual natural language processing pipeline, and classifies the input across 133 disease categories using an ensemble of machine learning models. Three classifiers were rigorously evaluated: Random Forest, XGBoost, and a Deep Neural Network (DNN). The DNN achieved the highest classification accuracy of 91.97%, with macro-averaged precision of 92.1%, recall of 91.8%, F1-score of 91.9%, and ROC-AUC of 0.994. The system further integrates GPS-based geolocation for nearby healthcare facility discovery and maintains longitudinal health records per authenticated user. Ethical safeguards are embedded throughout: the system explicitly positions itself as a decision-support aid, not a diagnostic replacement. Experimental results confirm strong per-class discrimination across all 133 disease classes with minimal inter-class confusion. SwasthyaVani demonstrates that accessible, language-native AI tools can meaningfully improve health-seeking behavior in underserved rural communities.
CIMAS HealthMate is proposed, a hybrid multilingual VHA that integrates transformer-based natural language processing (NLP) with an explainable extreme gradient boosting (XGBoost) decision model to provide accurate and transparent symptom triage.
Shamiso Simango, M. Mutandavari· Computer Science and Informa...· 0 citations
An AI-assisted healthcare kiosk, a web-based, fully offline system that provides automated disease prediction, medication recommendations, BMI assessment, cardiovascular risk evaluation, and doctor referrals without needing a permanent physician or internet connection is discussed.
Thanu Shree M. N, M. Vijayalakshmi· International Research Journ...· 0 citations
This work introduces IndicMedQA, a novel multimodal AI framework that integrates Indic large language models (LLMs) and visual encoders to analyze patient inquiries using both textual and visual cues, and creates a multilingual multimodal medical corpus spanning seven major Indian languages, translated using a semi-automated approach.
Akash Ghosh, Arkadeep Acharya, M. Muhsin et al.· ACM Transactions on Computin...· 2 citations
This study compares the performance between traditional feature-based classification methods and transformer architectures in mapping stress, anxiety, and depression conditions in Indonesian-language mental health discourse. The task is formulated as a multi-class classification problem, where each consultation is assigned a single dominant mental health category. By implementing an integrated experimental framework on an online consultation dataset, we tested Gradient Boosting as the baseline model against two specific transformer models, namely IndoBERT and IndoRoBERTa. Experimental findings indicate that transformer-based models consistently outperform traditional approaches, with IndoRoBERTa achieving the highest accuracy of 82%. These results affirm the capability of contextual language representation in capturing complex semantic and linguistic nuances in mental health texts. Nevertheless, this study notes ongoing challenges in differentiating categories with strong semantic overlap, particularly between stress and anxiety symptoms.
Evi Dwi Wahyuni, Wiwik Anggraeni, Reza Fuad Rachmadi et al.· International Seminar on Int...· 0 citations
A novel benchmark comprising over 9,000 real-world, point-of-care, multilingual, and multimodal clinical question-answer pairs sourced from frontline health workers in Nigeria reveals several critical insights into the suitability of LLMs as clinical decision support systems in low-resource contexts.
Tobi Olatunji, C. Aka, C. Okocha et al.· medRxiv· 0 citations
Comparative analysis confirmed that the proposed bilingual NLP model outperforms existing monolingual and rule-based systems in linguistic inclusiveness and accessibility.
