Revising Terminology and Phenotype Definitions for AI-Based Decision Support in Fluid Therapy: Call for Consensus

Abstract Intravenous fluid therapy is a cornerstone of optimizing body hydration, circulation, and perfusion, yet its rational implementation is limited by inconsistent clinical terminology. As fluid therapy moves toward AI-empowered clinical decision support systems (AI-CDSS), there is a critical need for high-quality, verified training data. The reliability of these systems is currently threatened by misleading data resulting from ambiguous definitions and the interchangeable use of nonsynonymous terms, such as “dehydration” and “hypovolemia.” This multidisciplinary panel-of-experts opinion article intends to start a discussion by identifying the key drawbacks in current terminology and proposes solutions, including a shift toward adequacy-based phenotypes and categorizing blood and water volumes as “insufficient,” “optimal,” or “excessive” rather than relying on context-sensitive quantitative measures such as “low,” “normal,” or “high.” Illustrative scenarios are used to show how inconsistent terminology and static quantitative labels could mislead an AI-CDSS. The proposed phenotype matrix is designed to provide a structured, systematic description of fluid requirements for specific clinical objectives that can be integrated into clinical algorithms and machine learning databases. An example of a fluid requirement assessment algorithm is used for exploring its applicability in AI training. The proposed terminology and phenotype matrix are provisional: they represent the opinion of a multidisciplinary expert panel and have not undergone formal consensus, clinical validation, or testing within an AI system. Broad consensus-building initiatives will follow.