Authors : Vinay patel; Deepesh Dewangan; Reena Sahu

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/5vfhfvez

DOI : https://doi.org/10.38124/ijisrt/25may2277

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The rapid evolution of artificial intelligence (AI) has significantly influenced various sectors, with healthcare being one of the most impacted domains. This report presents the development and implementation of an AI Healthcare Chatbot designed to assist patients and healthcare providers by delivering instant, accurate, and reliable medical information. The chatbot leverages natural language processing (NLP) and machine learning (ML) techniques to simulate human-like conversations, enabling users to inquire about symptoms, medications, diseases, and basic health guidelines. It serves as a virtual assistant capable of functioning 24/7, reducing the burden on healthcare professionals and enhancing patient engagement and accessibility. This project explores the methodologies involved in building the chatbot, including data preprocessing, intent recognition, entity extraction, and response generation. Various tools and technologies such as Python, TensorFlow, and Dialogflow are utilized to create a responsive and context-aware system. The report also evaluates the system’s performance through test cases and user feedback, demonstrating its potential as a reliable supplementary tool in primary healthcare delivery.

Keywords : Automatic Artificial Intelligence, Healthcare Chatbot, Natural Language Processing, Machine Learning, Automatic Diagnosis, Virtual Assistant, Symptom Checker, Medical Chatbot, Health Informatics, Patient Engagement.

References :

1. Yadav, R., & Gupta, M. (2020). Customer satisfaction in the restaurant industry: A review of literature. Journal of Foodservice Business Research, 23(3), 245-266. doi:10.1080/15378020.2020.1748346.
2. Kim, H.J., Lee, Y.J., & Kim, J.H. (2019). A study on the relationship between service quality and customer satisfaction in the restaurant industry. International Journal of Hospitality Management, 77, 76-83. doi:10.1016/j.ijhm.2018.06.003.
3. Kumar, L., & Singh, A. (2021). Exploring customer satisfaction through service quality in restaurants: A qualitative analysis. International Journal of Hospitality & Tourism Systems, 14(1), 39-49.
4. Prakash, S., & Sharma, A. (2022). Enhancing customer experience using NLP in restaurant order management systems. Journal of Business Research, 134, 352-360. doi:10.1016/j.jbusres.2021.08.052.
5. Cambria, E., Poria, S., Gelbukh, A., & Thelwall, M. (2017). Sentiment analysis is a big suitcase. IEEE Intelligent Systems, 32(6), 74-80. doi:10.1109/MIS.2017.4291173.
6. Sutskever, I., Vinyals, O., & Le, Q.V. (2014). Sequence to sequence learning with neural networks. In NIPS. doi:10.5555/2969033.2969173.
7. Mehta, S., & Ghosh, S. (2021). The impact of voice ordering systems on customer satisfaction in restaurants. International Journal of Hospitality Management, 95, 102945. doi:10.1016/j.ijhm.2021.102945.
8. Alavi, A., & Kahn, F. (2020). Machine learning applications in the food service industry: A systematic review. Journal of Foodservice Management & Education, 14(1), 27-39.
9. Chen, M., & Zhao, Y. (2021). A survey of sentiment analysis for restaurant reviews. Computers in Human Behavior, 115, 106616. doi:10.1016/j.chb.2020.106616.
10. Jain, A., & Kumar, V. (2019). A study on the role of big data analytics in the food and beverage industry. Journal of Retailing and Consumer Services, 48, 277-287. doi:10.1016/j.jretconser.2019.02.018.