2 📄 Overview A sophisticated SaMD that leverages a patient's genetic profile, metabolic markers, initial treatment response data (e.g., early weight loss, glucose changes), lifestyle data from wearables, and EHR information to predict individual response to GLP-1 therapy and recommend optimal dose titration schedules. This aims to minimize trial-and-error, accelerate response, and personalize the therapeutic journey. Key technologies: 👤 Target users: 👍 Benefits Improved treatment efficacy through personalized dosing • Reduced time to achieve clinical goals (e.g., target weight loss) • Cost savings by avoiding ineffective treatments or prolonged suboptimal dosing • Enhanced patient satisfaction by optimizing outcomes Use bullets or new lines. 👎 Challenges Availability and integration of comprehensive genomic and clinical data • Robustness and interpretability of complex AI models • High regulatory scrutiny for predictive decision support influencing drug dosage • Ethical considerations around data privacy and potential for 'non-responder' labeling 📋 Regulatory & Validation Potentially Class IIb or Class III SaMD given its direct influence on drug dosage and treatment decisions. Will require extensive clinical trials to validate prediction accuracy and safety, along with a robust QMS and post-market surveillance plan.