Emotion-Aware Healthcare Chatbots Using Multimodal Deep Learning and Natural Language Understanding - Muhammad , Syed; Murtaza , Rameez; Jawwad, Yousuf (Al-Qanṭara, 2025)
The advent of modern technologies like Artificial Intelligence (AI), Internet of Things (IoT) and Deep Learning (DL) has ushered in a transformative era in healthcare, offering innovative solutions towards personalized healthcare by enhancing the quality of various medical services. Our proposed methodology involves the development of a BERT-based medical chatbot, leveraging cutting-edge deep learning technology to significantly enhance healthcare communication and accessibility. The traditional challenges faced by medical chatbots, such as imprecise understanding of medical conversations, inaccurate responses to jargon, and the inability to offer personalized feedback, are addressed through the utilization of Bidirectional Encoder Representations from Transformers (BERT). The performance metrics of our chatbot underscores its effectiveness. With an accuracy of 98%, the chatbot ensures a high level of precision in handling medical queries. The precision score of 97% attests to the accuracy and reliability of its responses. The AUC-ROC score of 97% indicates the chatbot's exceptional ability to predict specific diseases based on user queries and symptoms, showcasing its robust predictive power. Furthermore, a recall of 96% demonstrates the chatbot's capability to avoid missing cases in medical diagnoses, ensuring comprehensive coverage of potential conditions. The F1 score of 98% showcases the chatbot's proficiency in delivering accurate and personalized healthcare information, striking a harmonious balance between precision and recall. Our BERT-based medical chatbot not only addresses the limitations of traditional approaches but also achieves a remarkable performance with high accuracy, precision, predictive power, and comprehensive coverage, making it a valuable tool for advancing the quality of healthcare services.