Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
@article{205431,
author = {SHERI RAMCHANDRAREDDY and Rajesh Banothu and Harshita Kusunuri and Nithya Reddy Kura},
title = {An AI-Based Medical Chatbot Model for Infectious Disease Prediction},
journal = {International Journal of Innovative Research in Technology},
year = {2026},
volume = {13},
number = {1},
pages = {6872-6880},
issn = {2349-6002},
url = {https://ijirt.org/article?manuscript=205431},
abstract = {An AI-Based Medical Chatbot Model for Infectious Disease Prediction is proposed to support early identification of diseases and improve access to basic healthcare services. The system leverages artificial intelligence, machine learning, and natural language processing to interact with users, collect symptom information, and analyze health data in real time. By providing preliminary risk assessment and health guidance, the chatbot assists users in understanding possible infectious conditions without immediate dependence on healthcare professionals. The chatbot integrates a trained predictive model capable of identifying patterns associated with common infectious diseases based on user-reported symptoms. Through a conversational interface, the system delivers accurate, timely, and personalized responses while maintaining ease of use. This approach helps reduce the burden on healthcare systems, minimizes delays in diagnosis, and supports informed decision-making for patients. The proposed model aligns with Sustainable Development Goal 3 (Good Health and Well Being) by promoting early detection, preventive care, and healthcare accessibility, particularly in remote and underserved areas. Overall, the AI-based medical chatbot demonstrates potential as a scalable and cost-effective solution for enhancing public health monitoring and improving overall healthcare outcomes.},
keywords = {Artificial Intelligence (AI), Disease Prediction, Electronic Health Records (EHR), Healthcare Informatics, Infectious Disease Prediction, Long Short-Term Memory (LSTM), Machine Learning (ML), Medical Chatbot, Natural Language Processing (NLP), Symptom Analysis].},
month = {June},
}
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