AI-Powered Online Symptom Checkers: Enhancing Accuracy and Guiding Users to Appropriate Medical Care

Abstract

Online symptom checkers have emerged as valuable tools for individuals seeking health advice and guidance. Powered by artificial intelligence (AI) algorithms,these symptom checkers aim to provide more accurate and personalized health assessments, directing users to the appropriate level of medical care. This study presents an innovative architecture that combines Vector Database (VDB), Faiss similarity search algorithm, and GPT (Generative Pre-trained Transformer) API to analyze user inputs, identify intents, and generate tailored responses. The proposed system leverages the power of VDB to store symptom and test data as embeddings, enabling efficient similarity searches. The GPT model is employed to analyze user inputs, determining whether the user is
seeking symptom analysis, test suggestions, booking details, booking help, or general health queries. Based on the identified
intent, the Faiss algorithm searches for relevant tests and returns appropriate recommendations. The GPT model then generates
personalized responses by considering the user's input, suggested tests, intent, and a summary of the chat history. The chatbot also incorporates a memory mechanism to store conversation summaries, providing context for subsequent interactions. Experimental results demonstrate the effectiveness of the proposed architecture in delivering accurate and context-aware health advice, guiding users to the most suitable medical care options. This study highlights the potential of AI-powered online symptom checkers in improving healthcare accessibility, reducing unnecessary medical visits, and empowering individuals to make informed decisions about their health.