Jul 2026· International Journal of Innovative Research in Computer and Communication Engineering· Vol 14· 0 citations
TL;DR
SwasthAI delivers an accessible, technology-driven healthcare experience designed to serve communities with limited medical infrastructure by combining NLP-driven symptom analysis, transfer learning for medical image classification, intelligent scheduling with patient registration, and an emergency alert mechanism.
Abstract
Access to healthcare remains a critical challenge across rural and semi-urban regions of India, where patients encounter difficulties such as limited availability of specialist doctors, communication barriers due to regional language diversity, absence of early screening tools, and fragmented systems for booking medical consultations. These issues often lead to delayed treatment, worsened health outcomes, and unnecessary travel to distant urban hospitals. Swasth AI addresses these challenges through an intelligent healthcare platform that enables users to describe symptoms via text or voice in seven Indian languages, receive AI-powered preliminary health assessments, identify suitable doctors through a multi-factor ranking system, and book appointments with automated email confirmations. Additionally, the platform incorporates a deep learning module for image-based skin condition classification using a Convolutional Neural Network trained on clinical dermatology data. The system architecture employs React 19 with Next.js 15 for the user interface, a Python-based REST API with PostgreSQL for data management, TensorFlow.js for in-browser model inference, the Web Speech API for multilingual voice recognition, and EmailJS for patient notifications. A geolocation-powered hospital mapping feature helps users identify nearby healthcare facilities without reliance on external map services. By combining NLP-driven symptom analysis, transfer learning for medical image classification, intelligent scheduling with patient registration, and an emergency alert mechanism, SwasthAI delivers an accessible, technology-driven healthcare experience designed to serve communities with limited medical infrastructure.
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, Vijayalakshmi M M· International Research Journ...· 0 citations
HealthMate is presented, an intelligent, explainable AI chatbot framework designed for preliminary healthcare consultation that demonstrates rapid retrieval, robust natural language comprehension, and clear explainability without replacing professional medical diagnosis.
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Shamiso Simango, M. Mutandavari· Computer Science and Informa...· 0 citations
Many people in rural and underdeveloped places continue to face significant chal-lenges in accessing dependable, qualified medical advice. When professional help is unavailable, patients may be forced to rely on traditional home remedies or local health myths, delaying the prompt and precise diagnosis required for effec-tive treatment. We aimed to close this essential diagnostic gap by developing a practical, machine learning-driven system for symptom-to-disease prediction. Our approach is simple; it takes a user’s reported symptoms and immediately generates a prioritized list of the top five most probable matching illnesses. We rigorously tested the solution using a variety of machine learning and deep learning techniques, including Random Forest, Decision Tree, Support Vector Machines (SVM), and a Deep Neural Network. Our final, optimized model achieved a robust 90% accuracy on a public dataset of disease and symptom specifications. Crucially, a clinical expert in homeopathy reviewed our model’s output and validated its accuracy for delivering basic, initial medical guidance and supporting early triage decisions for patients.
Tasneem Kagzi, Dr.Urvashi Makwana, H. Kagdi· International Journal of Sci...· 0 citations
Access to specialist clinical expertise remains severely limited across sub-Saharan Africa, where physician-to-patient ratios can fall below 1:25,000 in rural settings. Existing AI-assisted diagnostic tools predominantly require reliable internet connectivity and high-specification hardware, rendering them impractical for frontline healthcare workers in district hospitals and health centres. This paper presents Aletheia, an offline-first clinical decision support system designed for low-resource healthcare contexts across sub-Saharan Africa. Aletheia is built upon Qwen2.5-3B-Instruct, fine-tuned using Quantised Low- Rank Adaptation (QLoRA) on a curated dataset of 27,000 clinical reasoning samples spanning 50 disease conditions with elevated prevalence in East Africa. Evaluation demonstrates a Top-1 diagnostic accuracy of 80.0%, Top-3 accuracy of 100.0%, BERTScore-F1 of 0.909, and METEOR of 0.467 across ten representative clinical case categories. The system achieves an Expected Calibration Error (ECE) of 0.275 and passes the Africa Deep Tech Challenge 2026 (ADTC 2026) memory budget constraint of 7 168 MB, achieving a peak inference RAM of approximately 3 630 MB on the standardised benchmark laptop. These results demonstrate the feasibility of deploying large language model-based clinical reasoning at the primary care level in resource-constrained settings without cloud infrastructure.
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Akash Ghosh, Arkadeep Acharya, M. Muhsin et al.· ACM Transactions on Computin...· 1 citation