Volume 2 - Issue 3, May - June 2026

Artificial Intelligence has significantly transformed healthcare systems through intelligent diagnosis, predictive analytics, and conversational medical assistance. Traditional healthcare support systems often require manual interaction and are limited in providing instant preliminary diagnosis and patient guidance. This paper presents an Intelligent Medical Diagnosis Assistant using Deep Learning and Natural Language Processing for conversational healthcare support. The proposed system integrates Natural Language Processing, symptom analysis, deep learning classification, and conversational AI to assist users in identifying possible medical conditions based on symptom descriptions. The architecture combines a chatbot interface, symptom extraction module, disease prediction model, and recommendation engine to provide real-time medical guidance. The system uses Deep Learning algorithms for disease prediction and Natural Language Processing techniques for understanding user symptom descriptions. The developed assistant provides preliminary diagnosis suggestions, precaution recommendations, and healthcare guidance through conversational interaction. Experimental evaluation demonstrates improved prediction accuracy and efficient user interaction compared to traditional rule-based healthcare systems. The proposed approach provides a scalable, accessible, and intelligent healthcare support framework suitable for telemedicine, online healthcare consultation, and smart medical assistance applications.

Peese Vamshi, Dr.D.William Albert, G Sree Ramulu,"Intelligent Medical Diagnosis Assistant Using Deep Learning and Natural Language Processing" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(3): Page(618-620) May-June 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.