DermAI: An Innovative AI-Driven Chatbot for Enhanced Dermatological Diagnosis and Patient Interaction - Rajeshkumar, Pradeep; Kharche, Shubhangi; Poojari, Prithvi; Utekar, Sachet; Saini, Sahil; Bidwai, Samriddhi (IAES Indonesian Section, 2024)
Skin disorders constitute a noteworthy public health concern globally, with earnest impacts on both physical and mental well-being. However, effective dermatological care faces challenges in resource-limited regions due to poor infrastructure, limited access to medical facilities & expertise, and inadequate advanced diagnostic tools. The existing research work majorly focuses on cancer and uncommon skin diseases with models trying to achieve a higher training accuracy with no regards to misclassification rate. The products currently available in the market provide a limited initial diagnosis and suggest consulting a doctor to get an accurate diagnosis or offer a list of other possible skin disorders. To address these challenges, we propose DermAI, an innovative AI-based Chatbot made entirely of open-source technologies, which integrates the ResNet50 model and LLM via Chainlit, with Retrieval Augmented Generation(RAG), utilising AstraDB vector database and OpenAI embedding model for personalised responses. enabling accurate classification of common skin diseases. The proposed DermAI ensures minimal misclassification and comprehensive coverage of diseases, leveraging Retrieval-Augmented Generation and comparative model analysis. The metrics indicate that the model has a high true positive rate, with a misclassification rate of 2.17%, mean sensitivity, specificity & AUC of 92.6%, 99.8% & 99.9% respectively. This is demonstrated in the situations of melanoma, chickenpox, shingles, impetigo, and nail fungus, where it obtained 100% validation accuracy, a feat not attained by previous studies. Additionally, the model is highly capable of correctly identifying negative cases. The hallucination metric suggests the model may have a minimal tendency to hallucinate as the average hallucination score of 7% which falls far within the manually set threshold value of 50%. By setting the threshold value to 50%, the model generates grounded answers that are pertaining to the knowledge base and also allows it to be flexible with its responses. Overall, DermAI outperforms all solutions proposed in research literature.