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{204437,
        author = {Rupali D. Nawkhare and Prof. Kiran Pustode},
        title = {AI-Based Medical Symptoms Checker Using Natural Language Processing},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {2763-2769},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204437},
        abstract = {Artificial Intelligence is fundamentally transforming the modern healthcare sector by facilitating the development of highly intelligent and universally accessible medical assistance systems. This research presents the comprehensive design and deployment of an AI-powered medical symptom checker, an innovative framework engineered to help users identify potential illnesses by interpreting physiological symptoms described in natural, colloquial language. Unlike traditional diagnostic tools that constrain patients with rigid, predefined questionnaires and restrictive drop-down selections, the proposed system leverages advanced Natural Language Processing and Large Language Models to establish an intuitive, conversational user interaction. The application is constructed utilizing a robust technology stack encompassing Python, the Flask web framework, and various Machine Learning techniques, ensuring a lightweight, scalable, and highly responsive deployment across multiple internet-enabled devices. The central objective of this research is to significantly improve global healthcare accessibility, minimize unnecessary and costly hospital visits for minor ailments, and deliver rapid, context-aware preliminary health guidance. To foster user trust and algorithmic transparency, the system is equipped with advanced features, including the retention of contextual chat history for iterative symptom refinement and probabilistic confidence scoring to quantify diagnostic certainty. Ultimately, the proposed model demonstrates exceptional efficiency and user-friendliness while remaining firmly anchored in ethical AI deployment, explicitly functioning as a supportive decision-making resource rather than a definitive replacement for professional clinical diagnosis.},
        keywords = {Artificial Intelligence, Medical Symptom Checker, Natural Language Processing, Large Language Models, Machine Learning, Digital Healthcare, Flask, Web Application.},
        month = {June},
        }