Privacy-Preserving AI for Patient-Specific Drug Recommendation

This paper presents a privacy-preserving drug rec- ommendation system that leverages patient symptom inputs and advanced machine learning techniques to predict potential diseases and provide personalized treatment suggestions. The system ensures the secure handling of user data by incorporating privacy-preserving AI methodologies such as data minimization, local processing, and encryption techniques. Additionally, it maintains timestamped patient histories in the IST timezone to support continuity of care without compromising sensitive information. The proposed approach integrates a user-friendly interface with a robust, privacy-sensitive backend to enhance healthcare accessibility and build trust, particularly in remote or underserved regions.