TY - GEN
T1 - Integrating AI Chatbots with Cloud Computing for Managing Infectious Disease Symptoms and Guidance
AU - Vimaladevi, S.
AU - Sashi Rekha, K.
AU - Karthick, R.
AU - Sujitha, R.
AU - Chairmadurai, K.
AU - Sathishkumar, V. E.
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Managing infectious illnesses has been completely transformed by combining Artificial intelligence (AI) chatbots with cloud computing, which provides real-time symptom evaluation and guidance. This paper introduces a new method for improving the performance of AI chatbots hosted in the cloud using Bidirectional Encoder Representations from Transformers (BERT). Due to advanced natural language processing (NLP) capabilities, BERT can accurately comprehend and interpret user-reported symptoms, allowing it to provide personalized replies and recommendations. The proposed system offers scalable, safe, and efficient process computing. The chatbot can respond quickly and accurately to large volumes of queries. It uses cloud computing to process them all at once. Integrating BERT allows symptoms to be easily understood, distinct illness indications can be distinguished, and personalized advice based on the most recent medical knowledge can be provided. The system's goal is to improve illness management by letting people evaluate their symptoms and get the right counsel without having to see a doctor right away. This method encourages quick action and reduces needless visits, which is good for healthcare systems. The results analyze the system's performance in real-world situations and delve into the technical implementation. This study demonstrates how AI chatbots may enhance infectious disease management by significantly increasing the accuracy of symptom assessments and user satisfaction.
AB - Managing infectious illnesses has been completely transformed by combining Artificial intelligence (AI) chatbots with cloud computing, which provides real-time symptom evaluation and guidance. This paper introduces a new method for improving the performance of AI chatbots hosted in the cloud using Bidirectional Encoder Representations from Transformers (BERT). Due to advanced natural language processing (NLP) capabilities, BERT can accurately comprehend and interpret user-reported symptoms, allowing it to provide personalized replies and recommendations. The proposed system offers scalable, safe, and efficient process computing. The chatbot can respond quickly and accurately to large volumes of queries. It uses cloud computing to process them all at once. Integrating BERT allows symptoms to be easily understood, distinct illness indications can be distinguished, and personalized advice based on the most recent medical knowledge can be provided. The system's goal is to improve illness management by letting people evaluate their symptoms and get the right counsel without having to see a doctor right away. This method encourages quick action and reduces needless visits, which is good for healthcare systems. The results analyze the system's performance in real-world situations and delve into the technical implementation. This study demonstrates how AI chatbots may enhance infectious disease management by significantly increasing the accuracy of symptom assessments and user satisfaction.
KW - Healthcare Technology
KW - Infectious Diseases
KW - Personalized Advice
KW - Real-Time Guidance
KW - Symptom Management
UR - https://www.scopus.com/pages/publications/105013468572
U2 - 10.1109/ICCSP64183.2025.11089312
DO - 10.1109/ICCSP64183.2025.11089312
M3 - Conference contribution
AN - SCOPUS:105013468572
T3 - Proceedings of the 2025 11th International Conference on Communication and Signal Processing, ICCSP 2025
SP - 373
EP - 378
BT - Proceedings of the 2025 11th International Conference on Communication and Signal Processing, ICCSP 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th International Conference on Communication and Signal Processing, ICCSP 2025
Y2 - 5 June 2025 through 7 June 2025
ER -