Modern medicine has historically defined its primary object of inquiry as discrete, localizable disease. This ontology has been extraordinarily productive, but it is, by construction, positioned at a late point of observation: by the time diagnostic criteria are met, a long and largely silent biological trajectory has already been traversed, and the clinical event marks not the origin of a process but its clinically visible endpoint. We argue that medicine requires a new scientific object that precedes the category of “disease” one that is continuous, individual, and dimensional. We define this object as biological performance: the integrated, multi-system capacity of an organism to sustain, adapt to, and recover from perturbation over time. We then propose Continuous Biological Intelligence (CBI) as a scientific paradigm in which the primary object of clinical reasoning is not the instantaneous state but the lifelong trajectory of biological performance modeled continuously against an intra-individual baseline and interpreted through explainable, physician-supervised reasoning. We distinguish CBI from adjacent frameworks precision medicine, digital health, network medicine, systems/P4 medicine, and clinical artificial intelligence positioning AI as an implementation substrate of the paradigm rather than the paradigm itself. We formalize an operational architecture hypothesis whose layers emerge from, but do not define, the theory, and we introduce the biological state space as an explicitly falsifiable conceptual hypothesis. Throughout, unvalidated constructs are marked as such. The paradigm is offered not as established fact but as a falsifiable and generative conjecture: its central empirical claim that biological trajectories predict clinical outcomes better than cross-sectional measures is stated with an explicit refutation condition. Theory precedes evidence; the framework earns its standing only through the validation it invites. Keywords: continuous biological intelligence; biological performance; biological trajectory; longitudinal medicine; Clinical AI, healthspan; precision health; This preprint presents a conceptual scientific framework intended to stimulate empirical research. It introduces no new primary clinical data and proposes a falsifiable paradigm whose future validity depends on independent empirical testing. Version 1.0 (Founding Preprint) Permanent DOI:https://doi.org/10.5281/zenodo.21166376
MUSTAFA KÖROĞLU (Fri,) studied this question.