Mild Cognitive Impairment

Mild Cognitive Impairment (MCI) is a clinical state between normal cognitive aging and dementia. According to DSM-5, MCI involves significant cognitive decline in one or more domains (memory, language, executive function) while preserving independence in daily activities. In AI governance, MCI detection models must be evaluated against ISO 42001 AI Management System standards to ensure data-centric risk control. This is critical because biased training data could lead to discriminatory outcomes, violating the EU AI Act's principle of fairness. For enterprises, this means establishing a robust data-to-deployment pipeline that complies with both clinical standards and AI-specific regulations like the NIST AI RTO framework, which emphasizes trustworthiness and reliability in AI-driven healthcare solutions.

Enterprise application of MCI detection AI involves three critical steps: 1. Risk-adjusted development: Following ISO 42001 Clause 6.1.2, enterprises must identify risks associated with AI bias and data-centric risks before model deployment. 2. Privacy-preserving implementation: To comply with GDPR Article 22 and Taiwan's Personal Data Protection Act, companies must implement techniques like federated learning or differential privacy when handling retinal OCTA images. 3. Continuous monitoring: Establishing a feedback loop to track model performance (e.g., AUC-ROC, F1-score) against real-world clinical outcomes. A successful implementation should aim for a model AUC of at least 0.80 and a reduction in false negative rates by 15% within the first year of deployment, as demonstrated by the Eye-AD framework's external validation performance.

Taiwan enterprises face three primary challenges: Regulatory compliance, technical talent shortage, and stakeholder trust. First, the TFDA (Taiwan Food and Drug Administration)-regulated environment requires AI-based MCI tools to be certified as medical devices, necessitating compliance with the Medical Device Act. Second, the shortage of AI engineers with clinical domain expertise can be addressed by partnering with universities or medical centers. Third, the 'black box' nature of deep learning models like Eye-AD can be mitigated by adopting XAI (Explainable AI) techniques. The priority should be: Phase 1 (0-3 months) - Regulatory roadmap and data-centric risk assessment; Phase 2 (3-9 months) - Model development and TFDA certification; Phase 3 (9+ months) - Pilot deployment and continuous monitoring. This structured approach ensures compliance with both local law and international standards like ISO 42001.

Winners Consulting Services Co., Ltd. specializes in Mild Cognitive Impairment for Taiwan enterprises, delivering compliant management systems within 90 days. Free consultation: https://winners.com.tw/contact