Artificial Intelligence Brain Fog and Artificial Intelligence Brain Fry in Healthcare and Their Implications for Clinical Reasoning

Abstract

Artificial intelligence (AI) is being integrated into clinical documentation, decision support systems, healthcare education, and perioperative risk modelling. AI may enhance data synthesis, reduce administrative burden, and support individualized prediction, but it may also create cognitive risks that remain poorly defined in healthcare ethics. This narrative conceptual review distinguishes two emerging, non-diagnostic educational constructs. “AI brain fog” denotes diminished independent reasoning, recall, and clinical synthesis associated with habitual cognitive offloading to AI. “AI brain fry” denotes cognitive fatigue and overload caused by excessive AI outputs, alerts, monitoring, and verification work. The terms are not formal medical diagnoses; “brain fog” already has established clinical meanings, including in post-coronavirus disease 2019 (COVID-19) literature. The distinction is practical rather than absolute: fog is characterized primarily by under-engagement in reasoning, whereas fry is characterized by excessive cognitive demand, and both may occur together when overloaded clinicians default to shallow acceptance of AI output. Drawing on cognitive load theory, automation bias, cognitive offloading, alert fatigue, out-of-the-loop performance, healthcare education, and human–AI interaction, this review proposes observable markers, immediate remediation strategies, an interprofessional cognitive resilience framework, implementation responsibilities and metrics, and measurable hypotheses for prospective validation. Safe AI adoption requires technical evaluation alongside intentional protection of human judgment, diagnostic humility, attention, and professional accountability.