OPP_ALS_002 📄 Overview Develop an AI-powered SaMD platform that integrates real-world data (electronic health records, claims data, patient registries) with genetic and baseline digital biomarker data to identify optimal patient cohorts for ALS clinical trials. This platform would also leverage sophisticated matching algorithms to create statistically robust synthetic control arms, significantly reducing the need for placebo groups in certain trial phases and accelerating recruitment. Key technologies: 👤 Target users: 👍 Benefits Faster patient recruitment and enrollment • Reduced trial sample sizes • Improved statistical power through more homogeneous cohorts • Potential for fewer patients in placebo arms (ethical benefit) • Reduced trial costs and duration Use bullets or new lines. 👎 Challenges Access to diverse and high-quality real-world data • Data standardization and interoperability across sources • Regulatory acceptance of synthetic control arms • Ethical implications of AI-driven patient selection • Transparency and explainability of AI models 📋 Regulatory & Validation Regulatory bodies (e.g., FDA) are exploring frameworks for synthetic control arms; adherence to evolving guidance is crucial. • Robust validation of AI algorithms for bias, accuracy, and generalizability. • Clear data governance and privacy protocols are non-negotiable.