Abstract - Healthcare accessibility continues to be one of the most pressing global challenges, especially in rural and underdeveloped regions where people face delays in receiving timely medical assistance. Existing healthcare services are often fragmented, with limited disease prediction capabilities, poor support for local languages, and no mechanism to reuse patient medical data. These challenges result in late diagnosis, increased treatment costs, and poor continuity of care. This project proposes an AI-Based Multi-Service Healthcare Platform that integrates advanced Machine Learning (ML) and Natural Language Processing (NLP) techniques to provide real-time, intelligent, and accessible healthcare services. The platform introduces multiple modules including: • Disease Doctor – AI-driven disease prediction from symptoms and medical reports. • Medical Chatbot – 24/7 conversational healthcare guidance. • AI Dietitian – Personalized nutrition and lifestyle recommendations. • Digital Clone – A reusable health profile that securely stores patient records for future consultations. The system is developed using Flask (backend), React (frontend), MySQL (database), and Ollama Phi AI models for disease prediction and chatbot responses. It is designed to operate in low-resource settings, provide multi-language support, and ensure scalability for widespread adoption. By offering early disease detection, report interpretation, multi-language support, and continuous patient data management, this project demonstrates how AI can significantly improve healthcare delivery, reduce costs, and empower patients and doctors with smarter, data-driven medical insights. This comprehensive approach seeks to provide scalable, accurate, and user-friendly healthcare solutions, addressing the growing demand for digital health services. Keywords: Healthcare Website, Patient Management System, Online Appointment Scheduling, Electronic Health Records, Telemedicine Integration, User-Friendly Interface, Medical Data Security, Health Analytics, Responsive Web Design, Digital Health Solutions.
Prem et al. (2025) studied this question.