Abstract
In the realm of modern healthcare, the exigency for instantaneous, accurate, and personalized medical assistance remains a paramount concern, primarily due to infrastructural deficits and delayed medical interventions, especially in remote locales. The AI-ML-powered healthcare chatbot aspires to ameliorate this predicament by offering 24/7 medical support, ranging from symptom assessment to scheduling consultations and medication reminders, thereby fortifying healthcare accessibility. The focal point of this inquiry entails a meticulous exploration of the underlying factors contributing to delayed medical responses, analysing the causative elements, and investigating the contextual framework to comprehend when and how such exigencies materialize. A comprehensive literature survey will be undertaken to discern prevailing methodologies and state-of-the-art techniques employed for the detection and prevention of health-related complications through chatbot interventions, leveraging machine learning and natural language processing paradigms. Subsequently, a comparative analysis of these techniques will be conducted to delineate their respective efficacies, performance metrics, and limitations, facilitating the selection of the most optimal strategy for deployment in real-world scenarios. Ultimately, the inquiry will culminate in a decisive conclusion that encapsulates the key findings, underscores the transformative potential of AI-ML chatbots in healthcare delivery, and delineates actionable recommendations for future enhancements and scalability . Keywords—Healthcare Chatbot, Artificial Intelligence (AI), Machine learning (ML), Disease detection, Healthcare automation, Symptom Assessment
Published Version
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