In PMI-CPMAI,data suitabilityfor an AI use case is evaluated against the problem context and the populations affected. For a healthcare diagnostic AI system, this includes confirming that the training and evaluation data adequately represent therange of medical conditionsand thediverse demographics(age, gender, ethnicity, comorbidities, etc.) of the patients who will be served. Insufficient demographic coverage can lead to biased diagnostic performance and safety risks.
The framework recommends performing structureddata profiling and stratificationto understand how records are distributed across key groups and conditions. Byperforming demographic analysis and stratifying patient data, the team can identify underrepresented segments, such as certain age brackets, minority populations, or rare but critical conditions. This allows them to detect gaps (e.g., very few samples for a particular group), assess generalizability, and plan remediation (additional data collection, augmentation, or cautious deployment with guardrails).
While longitudinal and cross-sectional study designs (options A and D) are useful research concepts, the immediate need here is to check whether the current dataset spans the necessary demographic and clinical diversity. Analyzing variance and balance (option C) is helpful but too generic; the question explicitly references demographics. Thus, the most effective method to assure data suitability for the diagnostic tool isdemographic analysis and stratification of patient data.
