CLINICAL DATA QUALITY: A PREREQUISITE FOR TRUSTWORTHY AI IN HEALTHCARE

Artificial intelligence applications in clinical healthcare are expanding rapidly across diagnosis, treatment planning, and resource allocation. The reliability of these applications depends fundamentally on the quality of the data on which they are trained and operated. This paper examines eight systematic categories of clinical data failure documented in electronic health record systems and widely used open research databases, and proposes structured AI-assisted validation as a necessary precondition for trustworthy clinical AI deployment. The categories examined include erroneous entries, missing data, internal inconsistencies, duplicate patient records, statistical outliers, dataset bias, and temporal inconsistencies. For each category, specific clinical risks are identified and AI-assisted remediation strategies are proposed. The paper further argues that explainability is not optional in clinical AI validation: flagging anomalies without interpretable reasoning is insufficient for clinical adoption. Data quality is a patient safety issue. It should be treated as such before AI systems are deployed in clinical decision support.

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#MIMIC, #PhysioNet, #EU Health, #NHS