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MedTech Innovations, a manufacturer of advanced medical devices, has developed an AI-powered diagnostic tool that assists physicians in identifying early-stage cardiovascular diseases. Initially, the tool demonstrated high accuracy in clinical trials. However, after deployment in several hospitals, the tool\'s performance has noticeably degraded, leading to an increase in false positives and false negatives. This decline in accuracy has raised concerns among medical professionals and regulatory bodies, potentially violating patient safety protocols and regulatory compliance. A root cause analysis reveals that the training data used to develop the AI model contains inconsistencies, inaccuracies (e.g., incorrect patient demographics, erroneous lab results), and incompleteness (missing medical history).\n\nConsidering the principles of ISO/IEC/IEEE 12207:2017 and the criticality of data quality in AI applications, which of the following strategies should MedTech Innovations prioritize to most effectively address the performance degradation of the AI diagnostic tool and ensure patient safety?
Prioritize the remediation of the training dataset used for the AI model by addressing inconsistencies, inaccuracies, and incompleteness to improve the model's learning and prediction accuracy.
Retrain the AI model with the existing dataset, adjusting the model's parameters and architecture to better fit the flawed data, hoping to compensate for the data quality issues.
Increase the size of the training dataset by incorporating data from additional hospitals and clinics, assuming that a larger dataset will automatically improve the model's performance, regardless of data quality.
Modify the AI algorithm to be more robust to noisy data, implementing techniques such as outlier detection and data smoothing, without addressing the underlying data quality issues in the training dataset.

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