Habibi, Parnian

Complex bidirectional interactions between the nervous and cardiovascular systems are integrated through the heart–brain axis, a physiological pathway that governs both neural regulation of cardiovascular function and cardiovascular influences on brain health. Emerging evidence indicates that even in the absence of overt disease, subclinical changes in the heart, aortic arch, and extracranial arteries contribute to brain injury and cognitive vulnerability. This review synthesizes current knowledge on how subclinical heart–brain axis dysfunction affects brain structure and function, highlighting the roles of central and autonomic neural pathways as well as hormonal signaling in driving neurological decline. Key biomarkers linked to silent brain injury, cognitive decline, and dementia are discussed, emphasizing their potential for early risk stratification and as targets for preventive interventions. We also outline mechanistic pathways connecting subclinical heart–brain axis dysfunction to adverse brain outcomes, identify major gaps in current evidence, and propose priorities for future research and clinical trials aimed at early detection and risk reduction to preserve brain health.

Radiology is undergoing a paradigm shift from traditional single-function AI systems to sophisticated multi-agent networks capable of autonomous reasoning, coordinated decision-making, and adaptive workflow management. These agentic AI systems move beyond simple pattern recognition to encompass complex radiological workflows including image analysis, report generation, clinical communication, and care coordination. While multi-agent radiological AI promises enhanced diagnostic accuracy, improved workflow efficiency, and reduced physician burden, it simultaneously amplifies the long-standing “black box” problem. Traditional explainable AI methods, which are adequate for understanding isolated diagnostic predictions, fail when applied to multi-step reasoning processes involving multiple specialized agents coordinating across imaging interpretation, clinical correlation, and treatment planning. This paper examines how agentic AI systems in radiology create “compound opacity” layers of inscrutability from agent interactions and distributed decision-making processes. We analyze the autonomy–transparency paradox specific to radiological practice, where increasing AI capability directly conflicts with interpretability requirements essential for clinical trust and regulatory oversight. Through examination of emerging multi-agent radiological workflows, we propose frameworks for responsible implementation that preserve both diagnostic innovation and the fundamental principles of medical transparency and accountability.