Smart Healthcare Consultation System with Automated Doctor Mapping

Few rural and resource-limited settings enjoy access quality healthcare. Despite the rapid growth in telehealth systems, most of the current systems do not include intelligent triage, secure architecture validation, and experimentally tested AI integration. The paper is the design and experimental evaluation of a secure AI-based telehealth communication system that incorporates real-time consultation, automated doctor referral, and NLP-based symptom correction. It is built on the MERN stack and has a Naive Bayes classifier that is trained on structured medical data to help in initial triage. The experimental analysis with 50 simulated users proves that the system has a classification accuracy of 91.3, a system latency of an average of less than 250 ms, up time of 99.4% and assures compliance with AES-256 encryption. Comparative analysis reveals that the consultation time has been cut by 42 percent and healthcare expenditure on travel is also cut by 30 percent. The findings confirm that AI-based triage with a secure architecture can provide a substantial performance and scalability.