Doctors have been utilizing a sophisticated AI-driven cognitive solution to help with diagnosing illnesses.

PMI’s AI management guidance explains that models deployed in dynamic domains—such as healthcare—are particularly vulnerable todata drift, where “the statistical properties of input data or underlying real-world processes change over time, leading to performance degradation if models are not monitored and updated.” In the scenario, the cognitive diagnostic system is continuously exposed to new patient data and evolving medical knowledge from multiple databases. PMI notes that in such cases, “AI models that are not periodically retrained, recalibrated, or revalidated against current data will show reduced accuracy, reliability, and clinical usefulness over time.”

The final report states that the model’s performance degradedover time, affecting reliability and effectiveness, which is the hallmark symptom of data drift rather than an initial validation issue. PMI-CPMAI content stresses setting upcontinuous monitoring, performance dashboards, and drift detection mechanismsspecifically to track “the impact of data drift on model accuracy and business or clinical outcomes,” triggering model refresh or redesign when thresholds are exceeded. Changes in business model requirements could affect alignment of outputs to objectives but would not, by themselves, explain gradual technical degradation in predictions. Therefore, the most appropriate cause, as framed in PMI’s lifecycle and MLOps perspective, is theimpact of data drift on model accuracy, requiring ongoing monitoring and retraining to restore performance.