Ask-Audit-Apply: A Framework for Clinical Reasoning with Artificial Intelligence.

Advancing Trustworthy Clinical Decision Support Through Explainable Artificial Intelligence: A Framework for Transparent, Interpretable, and Ethical Healthcare Analytics

Artificial intelligence (AI) has become an integral component of clinical decision support systems, improving diagnostic accuracy, risk prediction, treatment planning, and healthcare resource management. But, the "black box" nature of many successful machine learning models has brought up concerns around trust and acco...

From Algorithm to Bedside: A Clinician's Framework for AI in Practice

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.

BEYOND ACCURACY: EXPLAINABILITY, WORKFLOW, AND GOVERNANCE IN THE CLINICAL IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE

Artificial intelligence (AI) is increasingly discussed as a transformative force in medicine, yet its practical value depends less on algorithmic promise alone than on whether tools can be integrated safely, intelligibly, and sustainably into clinical care. This review examines how free full-text PubMed literature desc...

Cognitive fog in AI-assisted care: Preserving clinical reasoning, patient education, and shared decision-making.

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.

Artificial intelligence in medicine: An ethical and legal review.

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.

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