Advancing Dermatological Care through AI: A Deep Learning-Based LINE Chatbot for Skin Disease Diagnosis - Enkvetchakul, Prem; Chawooram, Waranya; Pluempan, Adisorn; Noppitak, Sangdaow (Journal of Applied Informatics and Technology, 2025)
This paper presents the development and deployment of a LINE chatbot for diagnosing skin diseases using advanced deep learning techniques, addressing the challenge of timely and accurate diagnosis in resource-limited settings. While previous studies have explored convolutional neural networks (CNNs) for medical image classification, our research distinguishes itself by integrating MobileNetV2, DenseNet201, and EfficientNetB4 architectures within a mobile messaging platform. These CNN architectures are well-recognized for their robustness in image analysis, yet few studies have harnessed their potential in real-time diagnostic tools accessible to the general public via widely-used platforms like LINE. Our chatbot facilitates the immediate assessment of user-uploaded images, offering a proactive tool for managing skin health. By leveraging deep learning, we enhance diagnostic accuracy and reduce the reliance on traditional medical consultations. Experimental results indicate that our approach not only improves diagnosis precision but also extends the accessibility of dermatological care, particularly in underserved regions. This work contributes to the growing body of mobile health technologies by showcasing the transformative potential of AI-driven solutions in healthcare. The novelty of our research lies in the seamless integration of cutting-edge CNNs into an easy-to-use chatbot, enabling real-time, remote diagnostics in a practical, scalable manner.