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@article{171644,
        author = {Makam Nandeeswar and S.Harshith and Anna Reedy Venkata Sivananda Reddy and KothaCheruvuViswaTeja and Jaya Chandran D S and S Pravinth Raja},
        title = {Customer Support Chatbot with Ml},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {761-769},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171644},
        abstract = {Implementation of a Machine Learning-Powered Customer Support Chatbot in Healthcare Websites
The adoption of Artificial Intelligence (AI) and Machine Learning (ML) technologies within the healthcare industry has fundamentally altered service delivery mechanisms and patient interaction paradigms. A prominent application of this technological evolution is the development of a Customer Support Chatbot designed specifically for healthcare websites, which aims to improve patient care through real-time assistance, operational efficiency, and enhanced communication between patients and healthcare professionals. By utilizing advanced Natural Language Processing (NLP) techniques alongside sophisticated ML algorithms, this chatbot effectively addresses a diverse array of healthcare-related inquiries.
Operating continuously, the chatbot is available 24/7 to provide patients with immediate access to information and support. It is capable of responding promptly and accurately to questions regarding appointments, medications, symptoms, and general health inquiries. Furthermore, the system is engineered to deliver personalized experiences through the analysis of user interactions and historical data, fostering tailored communication that contributes to increased user satisfaction. The chatbot's utilization of NLP and predictive analytics facilitates preliminary health assessments, directing patients toward appropriate steps, such as scheduling consultations or seeking specialized healthcare advice.
An additional noteworthy characteristic of this chatbot is its integration with healthcare management systems, which enables the seamless scheduling, rescheduling, and cancellation of appointments. Moreover, patients can utilize the system to set reminders for medication, thereby promoting adherence to prescribed treatments and minimizing instances of missed doses. In light of the paramount importance of data privacy, the chatbot complies with rigorous regulations, including HIPAA, employing encryption and secure protocols to protect sensitive patient data.
The technical infrastructure supporting the chatbot incorporates robust ML libraries, such as TensorFlow and PyTorch, and advanced NLP tools like spacy and BERT. Its backend architecture is scalable, allowing for real-time processing and dependable cloud-based deployment. Continuous learning from user interactions enhances the chatbot’s accuracy and effectiveness over time facilitated through feedback mechanisms. This adaptive capability guarantees that the chatbot remains relevant and responsive to the changing needs of patients.
The deployment of this ML-powered chatbot exerts a significant influence on the healthcare ecosystem. By automating routine functions such as query handling and appointment management, the workload of healthcare professionals is reduced, allowing these providers to concentrate on delivering critical care. For patients, the chatbot contributes to quicker resolutions, improved engagement, and enhanced accessibility to health services, thereby fostering trust and satisfaction. This solution illustrates the capacity of AI to address deficiencies in the healthcare system, fostering a more patient-centered and efficient operational framework.
In summary, the Customer Support Chatbot utilizing ML represents a transformative advancement for healthcare websites, adeptly addressing the dual objectives of improving patient experiences and augmenting operational efficacy. Through the application of AI and ML technologies, it functions as an instrumental tool in bridging the communication gap between patients and healthcare providers, ultimately facilitating a more responsive, efficient, and patient-focused healthcare environment.},
        keywords = {Artificial Intelligence, Machine Learning, Natural Language Processing, Healthcare Chatbot, Symptom Analysis, Appointment Scheduling, Data Privacy, Healthcare Automation, Patient Engagement, HIPAA Compliance.},
        month = {January},
        }