OPP001 📄 Overview Develop a personalized digital twin for individuals with chronic conditions (e.g., diabetes, heart failure). This SaMD integrates real-time data from wearables (CGM, smart scales, BP cuffs), EHRs, genomics, and lifestyle inputs. An AI engine then predicts potential exacerbations, optimizes medication dosages in conjunction with clinical oversight, and delivers proactive, personalized behavioral nudges to prevent adverse events and improve adherence. Key technologies: 👤 Target users: 👍 Benefits Reduced hospitalizations and emergency visits • Improved chronic disease control and patient outcomes • Enhanced medication adherence and self-management • Personalized care pathways and treatment optimization • Significant cost savings for health systems and payers Use bullets or new lines. 👎 Challenges Achieving seamless, secure, and real-time data interoperability across disparate systems • Ensuring AI explainability and transparency for clinical trust and regulatory approval • Mitigating AI bias across diverse patient populations • Establishing clear clinical validation and RWE to support efficacy and reimbursement • User adoption and integration into existing clinical workflows 📋 Regulatory & Validation Likely SaMD classification as a diagnostic/treatment decision support system (Class II/III) • Requires robust clinical validation in target populations • AI algorithm validation for safety, effectiveness, and bias mitigation • Stringent cybersecurity and data privacy (HIPAA, GDPR) compliance • Consideration for adaptive AI algorithms and their regulatory oversight