000			02026nam a2200265 4500
001			ESSALUD
005			20260821110811.0
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040			_aBMG
041			_aeng
100			_aAbdulnour, Raja-Elie E. _eAutor _954227
245			_aCrew Resource Management — Navigating AI’s Automation Paradox
300			_apáginas: 731-734
520			_aViewed 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?
650			_aMEDICINA CLÍNICA GENERAL _953666
650			_aTECNOLOGÍAS DE LA INFORMACIÓN EN EL SECTOR SALUD _954228
650			_aCALIDAD DE LA ATENCIÓN _954152
650			_aPRÁCTICA MÉDICA, FORMACIÓN Y EDUCACIÓN EN GENERAL _954229
700			_aAkbarialiabad, Hossein _954230
700			_aHaghighi, Alireza _954231
700			_aLeachman, Sancy A. _954232
773	0		_022717 _922650 _dMassachusetts NEJM Group _oNEJM12 _tThe New England Journal of Medicine _wESSALUD _x0028-4793
942			_cARTICULOS _e2026-08-21 _zsqb
999			_c22849 _d22849