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Medical device teams often work across overlapping standards and regulatory frameworks. Automatan connects requirements across ISO, FDA, QMSR, MDSAP, and related references so teams can identify overlaps, gaps, and alignment opportunities.
This AI Transformation runs a bounded, SOP-governed search across regulatory, clinical, and public-sentiment sources to identify new safety and clinical information relevant to a specified contact lens device within a defined date range, classifies each finding against FDA and EU MDR reporting criteria, and produces a structured, source-attributed CER-oriented output — including reportability recommendations, non-conformance flags, trend analysis, and required stakeholder actions, for human Regulatory/QA review.
This AI Transformation analyzes a medical device Quality Manual against up to three uploaded regulatory and quality standards, converting manual content, regulatory references, documented processes, and standard clause text into structured, evidence-based cross-standard regulatory intelligence across 16 analytical dimensions. It surfaces key signals such as QMS scope and exclusions, the documented regulatory baseline, cross-standard requirement alignment and divergence, terminology relationships, requirement coverage status, traceability strength, prioritized coverage gaps, and standard edition consistency — each calibrated to how confidently it can be asserted from the provided evidence. This supports Regulatory Affairs leadership, Quality Assurance leadership, quality systems management, document control, internal audit, and executive oversight with faster, clearer, and fully traceable cross-standard mapping intelligence.