Abstract
This literature survey delves into the evolving realm of healthcare chatbots, analyzing a variety of research papers on AI chatbot systems. The papers cover diverse objectives, methodologies, and applications within healthcare, including microservice architectures for chronic patient support, disease prediction, herbal remedies, and mental health support. Common threads across the papers include the adoption of natural language processing, machine learning, and AI markup languages. Ethical considerations, such as data privacy and consent, are identified as crucial aspects. Some papers focus on specific medical domains, while others propose comprehensive frameworks integrating IoT and health knowledge graphs. The survey highlights challenges like unified semantic approaches for patient data and underscores the ongoing need for research to address gaps, enhance chatbot intelligence, and ensure ethical deployment in real-world healthcare scenarios
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