Abstract :

The Increasing Demand For Timely And Reliable Healthcare Information Has Encouraged The Development Of Intelligent Conversational Systems Capable Of Assisting Users In Obtaining Medical Guidance. This Paper Presents An AI-driven Medical Chatbot Designed To Answer Queries Related To Infectious Diseases Through Both Text And Voicebased Interaction. The Proposed System Employs A Long Short-Term Memory (LSTM) Neural Network Trained On A Structured Medical Question–answer Dataset To Recognize User Queries And Retrieve The Most Appropriate Responses. The Application Incorporates User Authentication, Multilingual Communication Support, Speech Recognition, Voice Response Generation, And Conversation History Management To Improve Accessibility And User Experience. A Sequence Of Preprocessing And Training Operations Is Performed To Prepare The Dataset And Optimize The Learning Model Before Deployment. The Trained Chatbot Is Capable Of Responding In Both English And Telugu, Enabling Wider Usability Among Diverse Users. Experimental Evaluation Indicates That The LSTM Model Achieves Approximately 99% Training Accuracy, Demonstrating Its Effectiveness In Identifying Relevant Responses For Disease-related Questions While Maintaining Low Prediction Error. The Integrated Framework Offers A Practical Solution For Delivering Instant Medical Information, Reducing Response Time, And Providing Accessible Healthcare Assistance Through An Interactive Web-based Environment. The Proposed System Can Serve As A Supportive Tool For Preliminary Health Guidance, Public Awareness, And Digital Healthcare Services While Improving Communication Between Users And Intelligent Medical Assistance Platforms.

Published:

30-7-2026

Issue:

Vol. 26 No. 7 (2026)

Page Nos:

1355-1360

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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