02026nam a2200265 4500 ESSALUD 20260821110811.0 ta t pe ||||| |||| 00| 0 spa d BMG eng Abdulnour, Raja-Elie E. Autor 54227 Crew Resource Management — Navigating AI’s Automation Paradox páginas: 731-734 Viewed through a relational lens, artificial intelligence (AI) has entered the clinical space as a copilot — a computer system that informs decision making by means of human-like interactions. AI copilots augment clinicians’ skills in the service of patient care. Indeed, systems based on large language models now draft clinical notes, generate accurate differential diagnoses, and propose management plans with increasing fluency. But this partnership poses a risk: the more capable the AI copilot appears, the more likely clinicians are to offload tasks to it, eroding the very skills they need for detecting when it’s wrong. The deskilled clinician — and especially the never-skilled or mis-skilled learner — is left exposed to AI’s fluency traps and silent failures. This “automation paradox” is a human hazard, compounding AI limitations such as confabulations, susceptibility to misinformation, and sycophancy. How can clinicians realize AI’s promise while avoiding its perils, and how should educators prepare trainees to take control when the copilot fails? MEDICINA CLÍNICA GENERAL 53666 TECNOLOGÍAS DE LA INFORMACIÓN EN EL SECTOR SALUD 54228 CALIDAD DE LA ATENCIÓN 54152 PRÁCTICA MÉDICA, FORMACIÓN Y EDUCACIÓN EN GENERAL 54229 Akbarialiabad, Hossein 54230 Haghighi, Alireza 54231 Leachman, Sancy A. 54232 22717 22650 Massachusetts NEJM Group NEJM12 The New England Journal of Medicine ESSALUD 0028-4793 ARTICULOS 2026-08-21 sqb 22849 22849