AI Health Care Chatbot System

Abstract

The main purpose of the healthcare chatbot system is to provide services in rural areas and government hospital for those people who are not able to take appointment or medical information from the doctors. They can solve their problem with the help of chatbot. With increasing population of India, increasing birth rate and decreasing death rate due to advancement in the medical field it has been observed that number of doctors are less to serve the need of the increasing population. This scenario can be better understood while walking through the cities government hospitals where the less availability of the doctors is the major cause behind the improper treatment of the patients and in certain scenario the resultant death so to encounter such cases there is a need of the smart and intelligent chatbot who can provide advice to the doctor and sometimes even to patient about what to do in such cases which ultimately results in the saving the life of hundreds of people. The AI based medical chatbot on which this project is based deals with providing medical advice in such scenario because sometimes doctors can even make mistake while observing the symptoms but the machine which is specifically developed for it cannot make such mistake. This AI based healthcare chatbot can take decision as per request of the patient. For this it uses its own database and in certain scenario where something is not available in its database as per request of the user, it collect the information from the search engine like google and give it to the user.

Introduction

The text describes the development of an AI-based healthcare chatbot system designed to improve accessibility and provide preliminary medical assistance to users through conversational interaction. Chatbots are introduced as an evolution of natural language processing systems, originating from early milestones such as the Turing Test and ELIZA. Their importance in healthcare is highlighted, particularly in supporting diagnosis, health education, and mental wellness guidance.

The proposed system aims to help users identify possible diseases based on symptoms, offer basic medical information, and respond to health-related queries in a user-friendly manner before consulting a doctor. It reduces pressure on healthcare professionals and improves access to basic healthcare support, especially in underserved regions.

The literature review shows that previous research has explored AI-driven medical chatbots using techniques such as deep learning, user dialogue systems, and domain-specific assistants like medication advisory bots. These studies demonstrate the growing relevance of conversational AI in healthcare but also highlight limitations in scope and personalization.

The problem statement emphasizes challenges in healthcare systems such as long waiting times, limited access to doctors, and overburdened medical staff. The chatbot addresses these issues by providing 24/7 automated support, symptom checking, and basic health guidance.

Methodologically, the system uses Python and key libraries such as Pandas, NumPy, and Scikit-learn for data processing and machine learning tasks. The dataset consists of 5,000–10,000 text-based medical queries categorized into symptom inquiry, medication information, appointment requests, wellness guidance, and escalation cases, split into 70% training and 30% testing data.

Conclusion

Currently artificial intelligent has developed to a point where programs can learn and effectively mimic human conversation. Chat bots have been around since 1966, but their popularity did not grow much until siri appeared in 2011 and then FB bot messenger. The market is constantly growing with many startups that recognize the potential for using chatbots in health care to support patients and providers. Just as cars measure getting down to drive themselves, care higher cognitive process is facing its own automation build, shortly patients are ready to enter their current symptoms through a portal, with the assistance of associate degree intelligent agent and find associate of degree correct designation or prescription while not involving a person’s doctor.

References

[1] Agarwal, R., Gao, G., DesRoches, C, “The Digital Transformation of Healthcare: Current Status and the Road Ahead”. Information Systems Research 21, 796-809 (2010). [2] Boukricha, H., Wachsmuth, “Modeling Empathy for a Virtual Human”, Volume 3, International Foundation for Autonomous Agents and Multiagent Systems, 1135–1136 (2011). [3] Stefano Marrone, Gabriele Piantadosi, Roberta Fusco, Antonella Petrillo, Mario Sansone, and Carlo Sansone,”A Novel Model-Based Measure for Quality Evaluation of Image Registration Techniques”, International Symposium on Computer-Based Medical Systems, IEEE,209–214,(2014). [4] Aron, E.N., Smollan, D, “Inclusion of Other in the Self Scale and the structure of interpersonal closeness”, Journal of Personality and Social Psychology 46, 895-902 (2015). [5] Gabriele Piantadosi, Stefano Marrone, Roberta Fusco, Antonella Petrillo, Mario Sansone, and Carlo Sansone,”Data-driven selection of motion correction techniques in breast DCE-MRI”,IEEE, 273–278, (2015). [6] Divya, Indumathi, Ishwarya, Priyasankari, “A Self-Diagnosis Medical Chatbot Using Artificial Intelligence”, proceeding MAT Journal, 1256-1264 (2017). [7] Flora Amato, Stefano Marrone, “Chatbots meet eHealth:automat zing healthcare”, Journal of Medical Internet Research, 115-123 (2018). [8] Chin-Yuan Huang, Ming-Chin Yang, Chin-Yu Huang, “A Chatbot-supported Smart Wireless Interactive Healthcare System for Weight Control and Health Promotion”, proceeding of the IEEE, 753-760 (2018). [9] International Journal of Scientific & Engineering Research Volume 12, Issue 7, July-2021 671 ISSN 2229-5518.

Copyright

Copyright © 2026 B. J. Gore, Sahil Shinde, Pranav Shinde, Ritesh Narte, Aarush Mengane. This 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.