Clinical Reasoning, Pathophysiology, and Artificial Intelligence in Critical Care: A Conceptual Framework for Integrating Decision-Making

Medical practice is an interpretive, decision-making activity conducted under uncertainty, and care quality depends on clinicians’ ability to organize knowledge into hypotheses, understand mechanisms, estimate probabilities, and revise decisions as the clinical course evolves. In critical care, where physiology is unstable and trajectories are nonlinear, these demands are magnified. We propose a conceptual framework integrating three complementary dimensions: clinical reasoning, which provides the cognitive architecture of decision-making; pathophysiology, which supplies the causal structure to interpret biological phenomena; and artificial intelligence, which, when well designed and critically used, can organize data, generate hypotheses, and reduce cognitive load. Tracing the evolution of clinical reasoning from the anatomoclinical model to hypothetico-deductive, probabilistic, cognitive, and sociotechnical models, we examine how diagnostic and therapeutic errors arise from interacting cognitive biases, system factors, and case characteristics, and how better problem representation, differential diagnosis, and team culture may mitigate them. We also frame Bayesian reasoning and decision thresholds as the probabilistic grammar of bedside decisions, analyze the risks of artificial intelligence, including automation bias, opacity, and limited metacognition, and propose principles for its safe use. Rather than replacing physicians, artificial intelligence should strengthen clinical reasoning and reinforce calibrated, accountable bedside judgment. These principles are intended as a framework for future research and require empirical validation before clinical translation.