The healthcare industry is changing emphasis on the enhancement of services through the implementation of new technologies. In this project, the researcher has suggested that a 24/7 healthcare chatbot system can be developed to guide the general population on primary medical care requirements. The chatbot will reduce the burden on the frontline healthcare practitioners, particularly in incidents of high demand or resource limitations, by offering available self-diagnosis and medical consultational services. As the demand to use medical services grows and the resources to do so remain unavailable, Healthcare Chatbot is an effort to make the life of common people in terms of primary care in health services easier by taking the front-line workers in the medical sector off their hands. This project work aims at designing a 24/7 accessible chatbot that will answer typical medical questions, predict the disease in accordance to the symptoms and radiology images given, and support precautionary measures that will be taken to prevent medication. A chatbot is able to offer a one-on-one interaction with customization in text to voice interface and offers a response. Using the methods of artificial intelligence and machine learning, the chatbot provides customized answers as it uses natural language processing to understand the user input in the form of keywords. It also reacts differently to message that contains some keywords and uses the concept of the Machine Learning to tailor their interactions to suit the case. The chatbot used in healthcare processes many requested queries simultaneously, and thus it is trustworthy to operate. The chatbot only answers medical questions as far as it can do so in accordance with the knowledge base. Although the chatbot is not supposed to substitute the work of a professional doctor, the chatbot is an instrument of self-diagnosis, which may ease the pressure on the medical resources, which is especially precious in terms of a pandemic when not all people have time to go to a doctor due to restricted access to physical care.

Here we have successfully created a Chatbot using Rasa. Rasa is an essential tool or Framework to build a Chatbot. The main advantage of Rasa chatbot is basically the easiness and customization of a chatbot without having in depth knowledge in deep neural networks and machine learning. In conclusion, the development of a smart health assistant with AI-driven self-diagnosis and disease forecasting capabilities is a complex but highly valuable endeavor in the realm of healthcare innovation. Through a combination of sophisticated algorithms, comprehensive testing methodologies, and user-centered design principles, such a system holds the potential to revolutionize the way individuals manage their health and well-being. By harnessing the power of artificial intelligence, this smart health assistant can accurately interpret user symptoms, analyze medical data, and provide personalized recommendations for self-diagnosis and disease forecasting. Whether it’s identifying potential health risks, suggesting preventive measures, or offering guidance on treatment options, the AI-driven capabilities of this system can empower users to make informed decisions about their health with greater confidence and efficiency. Throughout the development process, rigorous testing is paramount to ensure the reliability, accuracy, and safety of the smart health assistant. By employing both black box and white box testing methodologies, developers can thoroughly evaluate the system’s functionality, performance, and usability across a wide range of scenarios and use cases. This includes validating input handling, assessing boundary conditions, stress testing under high load conditions, and soliciting user feedback through acceptance testing.

1.Gilbert S, Mehl A, et al., “How accurate are digital symptom assessment apps for suggesting conditions and urgency advice? A clinical vignettes comparison to GPs.” BMJ Open. 2020. BMJ Open 2.Ćirković A, et al., “Evaluation of Four Artificial Intelligence–Assisted Self-Diagnosis Apps.” JMIR (and related outlets). 2020. JMIR 3.Fraser H, et al., “Comparison of diagnostic and triage accuracy of Ada Health and WebMD symptom checkers, ChatGPT, and physicians.” JMIR / mHealth and uHealth. 2023. PubMed+1 4.Azmi J., et al., “A Systematic Review on Machine Learning Approaches for Cardiovascular Disease Prediction using Medical Big Data.” 2022. PubMed 5.Kolasa K., et al., “Systematic reviews of machine learning in healthcare: a literature overview.” 2024. PubMed 6.Venkatramanan S., Sadilek A., Fadikar A., et al., “Forecasting influenza activity using machine-learned mobility map.” Nature Communications. 2021. Nature 7.Fraser H., “Safety of patient-facing digital symptom checkers” (Lancet commentary and follow-ups on Babylon Health). 2018–2019. The Lancet+1 8.Nalajala P, Lakshmi SB. A secured IoT based advanced health care system for medical field using sensor network. International Journal of Engineering & Technology. 2018;7(2.20):105-8. 9.Paparao Nalajala Godavarthi, Bhavana, Mohammad khadir,” Biomedical sensor based remote monitoring system field of medical and health care” Journal of advanced research in dynamical and control systems, Vol. 9, Issue 4, PP: 210-219, 2018.

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