This paper presents a personalized AI-driven diagnostic system specialized in internal medicine. The system is designed to predict potential diseases from user-generated natural language symptom descriptions. It incorporates a dual-layer symptom extraction module that combines large language models (LLMs) with a structured NLP-based symptom mapping table, overcoming the limitations of conventional fixed-choice input methods and enabling user-friendly free-form symptom entry. Unlike traditional symptom checkers, our model leverages user-specific health profiles—including gender, age, chronic conditions, and current medications—as inputs for a multi-stage disease prediction pipeline, enhancing diagnostic accuracy through personalization. The system generates top disease candidates accompanied by a quantified risk score and provides condition-specific actionable health guidelines, aiding user decision-making. Implemented as a mobile application using React Native and Node.js, the system enables real-time symptom entry, analysis, and personalized feedback. Its integrated architecture, supported by a dedicated AI server, ensures usability, responsiveness, and scalability in real-world healthcare settings. Experimental validation confirms the system’s effectiveness in delivering relevant and individualized insights for internal medical conditions, laying a robust foundation for developing intelligent, user-centered self-diagnosis tools in the digital health domain.