OPP001 📄 Overview A sophisticated SaMD leveraging multi-modal EMR data (structured labs, medications, diagnoses, and unstructured clinical notes via NLP) to identify patients at high risk for undiagnosed hypothyroidism. The system issues contextual, prioritized alerts to clinicians, providing an 'explainable AI' rationale (e.g., 'Patient X has TSH 5.2, fatigue documented 3 times in 6 months, and new onset depression - consider follow-up TSH/fT4'). Alerts are tiered based on confidence score and actionable insights, designed to minimize fatigue. Key technologies: 👤 Target users: 👍 Benefits Reduce diagnostic delay by 30-50% • Improve early treatment initiation • Lower rates of advanced hypothyroidism complications • Enhance adherence to clinical guidelines • Decrease unnecessary specialist referrals Use bullets or new lines. 👎 Challenges Overcoming alert fatigue for clinicians • Achieving high accuracy and specificity to avoid false positives • Seamless integration with diverse EMR systems • Data quality and completeness across different institutions • Physician trust and adoption of AI-driven recommendations 📋 Regulatory & Validation Likely Class II SaMD (diagnostic support system) • Requires rigorous clinical validation studies (prospective, retrospective) • FDA/CE Mark approval for diagnostic aid claims • Adherence to ISO 13485 and IEC 62304 standards