AI-Driven Medical Chatbot for Early Disease Detection

Abstract

This paper presents the design and implementation of an AI-powered medical chatbot for predicting infectious diseases and providing medical assistance. The chatbot leverages Machine Learning (ML) techniques, including Support Vector Machine (SVM), Long Short-Term Memory (LSTM) networks, and Natural Language Processing (NLP), to accurately interpret and respond to user queries. It is trained on medical datasets consisting of symptom descriptions, disease history, and treatment plans. Therefore, the chatbot can suggest an accurate diagnosis, preventive measures, and possible treatment options. The chatbot serves as an intelligent healthcare assistant that provides immediate replies and individualized medical indications at all times, minimizing the necessity for immediate physician consultations. It is intended to be used on various platforms to make it available through the internet and mobile application. The system has a high accuracy rate. As a result, it is effective in predicting diseases and engaging users. The purpose of the study is the role of AI chatbots in the transformation of healthcare. AI chatbots connect patients with medical professionals, provide immediate support, and alert patients of the early signs of diseases (especially during pandemics and emergencies).

Introduction

Artificial Intelligence (AI) and Machine Learning (ML) have significantly advanced healthcare through tools like medical chatbots, which use models such as Support Vector Machines (SVM), Long Short-Term Memory (LSTM) networks, and Natural Language Processing (NLP) to predict diseases and support patients. These AI-driven chatbots help address challenges posed by infectious diseases and pandemics by providing accessible, scalable healthcare solutions, reducing strain on medical staff, and improving early disease detection.

The literature review highlights several key studies on AI chatbots for disease prediction, showing high accuracy rates (up to 97.4%) using supervised learning methods and deep learning, though challenges like adaptability and real-time learning remain.

Traditional healthcare systems struggle with accessibility, delayed diagnosis, and resource limitations, especially in pandemics and remote areas. AI chatbots offer a promising solution but must overcome issues like accuracy, complexity in understanding queries, and data privacy.

The proposed chatbot system includes multiple modules for user interaction, symptom analysis, disease prediction, medical knowledge management, treatment recommendations, model training, security, and performance evaluation. The methodology involves data collection from diverse sources, preprocessing, NLP for user input understanding, and machine learning models (SVM, LSTM, Decision Trees) for prediction.

Evaluation results showed the chatbot achieves high accuracy (SVM at 97.4%), fast response times (~1.8 seconds), and positive user feedback, outperforming existing chatbots in speed and NLP capabilities. Challenges include handling complex symptoms and improving user trust. Future enhancements include Explainable AI for transparency, telemedicine integration, real-time data updating, and multilingual support to expand accessibility.

Conclusion

The AI-based medical chatbot developed in this study demonstrates the transformative potential of artificial intelligence in healthcare, particularly in disease prediction and early diagnosis. By integrating machine learning techniques such as Support Vector Machine (SVM) and Long Short -Term Memory (LSTM), along with Natural Language Processing (NLP), the chatbot efficiently analyzes user symptoms and provides accurate disease predictions with a high accuracy rate of 97.4%. The system not only enhances healthcare accessibility but also offers real-time symptom analysis, preventive measures, and treatment recommendations, reducing dependency on direct physician consultations, especially in remote and underserved areas. Despite its effectiveness, challenges remain in handling ambiguous symptoms, improving response adaptability, and ensuring data privacy. Future advancements will focus on expanding the chatbot’s medical knowledge base, integrating real-time telemedicine services, and implementing reinforcement learning techniques to enhance adaptability. Additionally, incorporating multilingual support will further improve accessibility across diverse populations. With continuous refinement and integration with modern healthcare systems, AI-driven chatbots can play a significant role in bridging the gap between patients and healthcare professionals, ultimately contributing to improved healthcare accessibility, early disease detection, and effective pandemic management.

References

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Copyright

Copyright © 2025 Pavani Mandepudi, Gade Sindhu, S S Rohan, Dr. L. Chandrasekhar Reddy, Parameswar M. 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.