Integration of Artificial Intelligence (AI) in healthcare has revolutionized patient care by increasing access, efficiency and diagnosis accuracy. This research focuses on the development of a smart healthcare chatbot, which takes advantage of natural language processing (NLP) and machine learning (ML) to help patients in symptomatic evaluation, medical inquiries and healthcare recommendations. The chatbot serves as a virtual health assistant, providing immediate reactions, initial diagnosis, and appointment scheduling and drug reminder. By analyzing the patient input, the system symptoms may suggest possible conditions based on the database and combine users with health professionals if necessary. The chatbot also increases telemedicine by reducing the response time, improving the patient's busyness and reducing the workload on medical physicians. The study evaluates the efficiency, accuracy and user satisfaction of the chatbot through real -world testing and response analysis. Results indicate that AI-operated healthcare chatbots can significantly improve the patient's experience, provide timely medical guidance, and streamline health services, making them become a valuable additional for modern healthcare systems. Future reforms may include integration with multilingual support for advanced AI-operated diagnosis and comprehensive access.
Python, Tensorflow or Pytorch, ML, Deep Learning, Pre-Trained Yolov10
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