Sep 2026· JAMIA Journal of the American Medical Informatics Association· 0 citations
Medicine
TL;DR
Limits in prevailing approaches to health AI evaluation are examined and a framework prioritizing outcomes-based, systems-level assessment aligned with healthcare delivery goals is proposed, contrasting a deontological focus on methodological standards with a consequentialist framework emphasizing real-world impact.
To promote the safe and effective adoption of AI, stronger clinical evidence, structured training programs, and organizational models capable of supporting its implementation are required.
Eugenio Santoro· Recenti progressi in medicin...· 0 citations
Cognitive safety is proposed here as a longitudinal property of the clinician-AI-organization sociotechnical system: its capacity to support or improve clinical performance without eroding independent hypothesis generation, uncertainty calibration, reasoned dissent, metacognitive control, and resilient performance when...
S. Corrao· Recenti progressi in medicin...· 0 citations
BACKGROUND
Generative artificial intelligence (Gen AI) is becoming increasingly used in mental healthcare. While these systems offer scalable, human-like interactions, concerns persist regarding safety, clinical validity, and the absence of systematic assessment frameworks.
OBJECTIVES
This study systematically review...
Polina Durneva, Hedieh Ghorbanie· International Journal of Med...· 0 citations
This narrative review synthesizes evidence on clinical AI methods, applications, implementation, evaluation, evaluation, and sustainability in hospitals and proposes institutional efficacy as the warranted and revisable hospital-level judgment that an AI-enabled service configuration can generate and sustain patient-ce...
The study found that existing health and medical practice regulations remain oriented toward conventional healthcare relationships, and shared liability as a legal construction for distributing responsibility in AI-related healthcare malpractice is found to be inadequate.
Mochammad Tanzil Multazam, Hindarto, Yusuf Handyliem et al.· 0 citations
This analysis suggests that, across CFIR domains, adaptive and generative AI systems foreground issues such as opacity, probabilistic outputs, dependence on local data ecosystems, performance drift, vendor-mediated updating, regulatory uncertainty, professional identity tensions and the need for calibrated trust.
Per Nilsen, Kathrine Hald, M. Neher· Implementation Science Commu...· 0 citations
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