Aug 2026· Journal of general internal medicine· 0 citations· 7 references
Medicine
TL;DR
Ask-Audit-Apply (AAA) is proposed as a clinician-facing framework for AI-supported clinical reasoning that specifies decision-level behavioral practices that may support education, supervision, and future research on safe AI integration across a range of CDS tools as these systems increasingly participate in clinical care.
A narrative review of the evolving image of XAI as a tool to create trustworthy clinical decision support systems and how it can be performed based on the concepts of transparency, interpretability and moral decision-making concludes that explainability is a fundamental component to the use of AI in everyday clinical p...
Rakesh Venuturumilli, Amoli Singh, Hemanshi Dhaduk et al.· European Journal of Prosthod...· 0 citations
This article proposes seven questions that clinicians can run through to evaluate any clinical AI tool in the time it takes to read an abstract, alongside a traffic-light schema for matching oversight to risk and a short list of demands clinicians should make of vendors and institutions.
Alaa Abdelqader, M. Alkhateeb, Abdullah Al-Marrawi et al.· Avicenna Journal of Medicine· 0 citations
A review examines how free full-text PubMed literature describes the real-world implementation of AI in healthcare and identifies the technical, organizational, ethical, and social conditions that shape adoption.
Jagoda Pałubska, Oliwer Műller, Zuzanna Rafałowska et al.· International Journal of Inn...· 0 citations
Effective mitigation must address both individual barriers, including time pressure, variable AI literacy, and reluctance to challenge automated output, and systemic barriers, including weak governance, opaque tools, misaligned incentives, and inadequate monitoring.
Jeffrey V. Esteron, Rhocette M. Sn Agustin, S. R. Y. Basilio et al.· Patient Education and Counse...· 0 citations
This review addresses the primary ethical principles relevant to AI in medicine - including respect for patient autonomy, beneficence, non-maleficence, and justice - alongside key legal frameworks with respect to liability, data protection, regulatory compliance, and algorithmic transparency.
Sebastian Schleidgen, O. Friedrich· European journal of internal...· 0 citations
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
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
