Clinical cognition in the age of cardiovascular AI

Abstract

Artificial intelligence (AI) is rapidly entering cardiovascular medicine through electrocardiography, imaging, wearable monitoring, risk prediction, heart failure management, and clinical decision support. Its value, however, should not be judged only by technical accuracy, speed, or computational sophistication. Cardiovascular care requires clinicians and teams to convert multimodal, longitudinal, incomplete, and context-dependent information into action under uncertainty. This Perspective argues that AI should be understood not as a replacement for clinical intuition, but as a cognitive instrument that reshapes how cardiovascular teams perceive, prioritize, reason, decide, communicate, and learn. Building on dual-process theories of clinical reasoning, the manuscript proposes that AI can support both rapid pattern recognition (System 1) and slower analytic reasoning (System 2), while also creating new vulnerabilities when automation bias, alert fatigue, poor explainability, dataset shift, hidden inequity, or responsibility drift distort judgment. The central standard should therefore move from algorithm-centered performance to accountable intelligence: AI that is accurate, explainable, locally validated, equitable, auditable, monitored across its lifecycle, and embedded within explicit clinical governance.