Portfolio Details

Abstract

SkinGPT represents a groundbreaking advancement in dermatological diagnostics, merging state-of-the-art machine learning and natural language processing to revolutionize the way skin diseases are diagnosed and understood. At its core, SkinGPT employs YOLOv5, an advanced image classification technology, which enables precise disease identification from digital images. Complementing this is the LLaMA model, utilized for its robust natural language generation capabilities, allowing SkinGPT to provide detailed, accessible explanations and health information through a user-friendly chatbot interface. This dual functionality facilitates real-time patient interaction, significantly enhancing patient engagement and satisfaction. Moreover, SkinGPT assists dermatologists by offering reliable second opinions, thus increasing diagnostic accuracy and confidence in treatment plans. By leveraging these technologies, SkinGPT not only streamlines the diagnostic process but also enhances the accessibility of dermatological care, particularly in underserved areas where specialist access is limited. This system sets a new standard for telemedicine applications in dermatology, offering a scalable solution that could be extended to other areas of medicine in the future.

• Collaborated on the development of SkinGPT, an AI-powered diagnostic tool for skin condition analysis, as part of an NLP course at George Mason University.
• Integrated YOLOv5 for image classification and the LLaMA model for natural language processing, creating a user-friendly chatbot for delivering accurate health insights.
• Enhanced dermatological care by providing reliable second opinions for dermatologists and improving patient engagement, particularly in underserved communities.

Key Highlights

• Image Classification with YOLOv5:

  Implemented a state-of-the-art image processing model for accurate classification of diverse skin conditions, ensuring real-time and reliable diagnostics.

• Natural Language Processing with LLaMA:

  Integrated robust NLP capabilities to generate user-friendly explanations and health recommendations, bridging the gap between complex medical information and patient understanding.

• User-Friendly Interface:

  Designed an intuitive chatbot interface using React and Bootstrap, enabling seamless interactions for patients and healthcare providers.

• Data-Driven Development:

  Utilized a diverse dataset of 2,384 dermatological images, employing advanced preprocessing and augmentation techniques to improve model robustness and reduce biases.

• Scalable Architecture:

  Built a modular system architecture using Python (Flask), ensuring scalability, data security, and efficient backend communication.