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Consider an organization developing an AI-powered diagnostic tool for medical imaging. During the risk assessment phase, a potential risk is identified: the AI model exhibits lower accuracy in diagnosing rare diseases due to underrepresentation in the training dataset. This could lead to delayed or incorrect diagnoses for patients with these conditions. According to ISO 42001:2023 principles for AI risk management, which of the following actions best reflects a proactive and compliant approach to addressing this identified risk?
Implement a continuous monitoring system to detect performance degradation on rare disease cases and establish a protocol for human expert review when the AI's confidence score falls below a predefined threshold for such cases.
Rely on the AI system's general high accuracy for common diseases, assuming that the impact of misdiagnoses for rare conditions is statistically insignificant and acceptable.
Immediately halt the development of the diagnostic tool until a perfectly balanced dataset for all possible diseases, including extremely rare ones, can be acquired, which may be practically impossible.
Document the risk of underperformance on rare diseases as a known limitation and proceed with deployment, assuming regulatory bodies will provide guidance on acceptable error rates for such scenarios in the future.

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