Abstract

AI and deep learning, which assess symptoms and diagnose, have improved healthcare.  The paper intends to create AI-based symptom analysis systems using deep learning models for accurate, efficient, tailored medical insights.  A differential diagnosis is made using advanced natural language processing NLP to assess user-reported symptoms, medical history, and other contextual information. Transformers, CNNS, and RNNs would be trained on big medical datasets. These models accurately identify complex symptom patterns and diseases.  In tests, transformer-based structures like transformers identify rare diseases and complex symptom combinations with over 92% accuracy.  The method handles overlapping symptoms using multi-label classification. The system's ability to process unstructured data from EHRs and patient reports to the Outcomes PRO is crucial. This permits thorough symptom extraction and analysis, reducing chart review and improving scalability. The system is also integrated into a simple web app that provides initial assessments and directs users to medical appointments. It is an AI-powered aid for patients and doctors, not a replacement.  Earlier warning indications for dangerous illnesses and faster diagnostics could improve patient outcomes, lower healthcare costs, and boost medical decision-making efficiency. This is a major step toward AI-based tailored medicine.

Associate editor: Hinda Gmati