OPP001_LUPUS_AI_FLARE_PREDICT 📄 Overview A SaMD-classified platform integrating continuous physiological data from wearables (sleep, activity, HRV, skin temperature), environmental data (weather, pollution), patient-reported symptoms (fatigue, pain, skin rashes), and EHR data. An AI/ML engine analyzes these multimodal inputs to identify individual flare triggers and predict an impending flare (e.g., 24-72 hours in advance) with high accuracy. The platform provides personalized alerts, guided interventions (e.g., stress reduction exercises, medication adjustment reminders, rest recommendations), and educational content to proactively manage or mitigate flare severity. Key technologies: 👤 Target users: 👍 Benefits Reduced frequency and severity of lupus flares • Improved patient quality of life and functional status • Reduced emergency room visits and hospitalizations • Enhanced patient self-efficacy and adherence to treatment plans • Personalized care pathways based on individual triggers Use bullets or new lines. 👎 Challenges Robust clinical validation across diverse patient populations • Interoperability with various EHR systems and wearable devices • Ensuring data privacy and security for highly sensitive health information • Mitigating algorithmic bias and ensuring equitable access • Maintaining sustained patient engagement with the platform 📋 Regulatory & Validation Likely Class II or III SaMD due to predictive and diagnostic/prognostic claims. Requires FDA/CE Mark clearance with rigorous clinical validation of accuracy, safety, and effectiveness. Clear data governance and cybersecurity protocols essential.