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Artificial intelligence holds promise for individualized medicine. Yet, transitioning models from prototyping to clinical applications poses challenges, with confounders being a significant hurdle. We introduce a two-dimensional confounder framework (Confound Continuum), integrating a statistical dimension with a biomedical perspective. Informed and context-sensitive confounder decisions are indispensable for accurate model building, rigorous evaluation and valid interpretation. Using prediction of hand grip strength (HGS) from neuroimaging-derived features in a large sample as an example task, we develop a conceptual framework for confounder considerations and integrate it with an exemplary statistical investigation of 130 candidate confounders. We underline the necessity for conceptual considerations by predicting HGS with varying confound removal scenarios, neuroimaging derived features and machine learning algorithms. We use the confounders alone as features or together with grey matter volume to dissect the contribution of the two signal sources. The conceptual confounder framework distinguishes between high-performance models and pure link models that aim to deepen our understanding of feature-target relationships. The biological attributes of different confounders can overlap to varying degrees with those of the predictive problem space, making the development of pure link models increasingly challenging with greater overlap. The degree of biological overlap allows to sort potential confounders on a conceptual Confound Continuum. This conceptual continuum complements statistical investigations with biomedical …