ArticleJournal of anesthesia, analgesia and critical care2025
AI policy in healthcare: a checklist-based methodology for structured implementation.
Article in Journal of anesthesia, analgesia and critical care, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI spring and its regulation discourse: a bibliometric study of trends in literature.Frontiers in artificial intelligence · 2026Pooled it
- Artificial intelligence in cardio-kidney-metabolic care: Transforming integrated disease management through data-informed innovation.International journal of obesity (2005) · 2026Review
- SurgiGuide: enhancing perioperative patient education with a rule-based digital agent in elective colorectal surgery.Journal of anesthesia, analgesia and critical care · 2026Article
- From promising prototypes to "instructions for use": embedding LLMs safely in perioperative and intensive care.Journal of clinical monitoring and computing · 2026Article
- Artificial intelligence in trauma care: applications, ethical challenges, and pathways toward responsible integration.Current opinion in anaesthesiology · 2026Article
- Artificial Intelligence Literacy in Intensive Care: From Algorithmic Fluency to Clinical Accountability.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2026Article
- Localized AI for stroke care in LMICs: a framework to overcome structural and diagnostic barriers.Frontiers in public health · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionArtificial Intelligence (AI) is transforming anaesthesia and intensive care medicine, enhancing diagnostic precision, workflow efficiency, and patient safety. However, deploying AI in high-acuity environments involves regulatory, ethical, and operational challenges. The European Union Artificial Intelligence Act (AI Act), effective 2025, imposes binding obligations on healthcare organizations, creating an urgent need for structured, governance-focused AI policies. This work presents a checklist-based methodology for responsible, safe, ethical, and regulation-aligned AI adoption in clinical units. THE NEED FOR A METHODOLOGY TO DEVELOP AN AI POLICY: Effective AI policies must ensure transparency, safety, fairness, and regulatory compliance while remaining adaptable to rapid technological and legislative changes. The proposed methodology employs a domain-specific checklist to generate critical evaluative questions, enabling healthcare professionals to systematically assess AI systems' appropriateness, reliability, and legal implications without relying on rigid, quickly outdated prescriptive rules. THE AI ACT AND ITS RELEVANCE: Regulation (EU) 2024/1689 establishes the first comprehensive AI legal framework, introducing risk-based classification, imposing stringent requirements for high-risk AI, often including medical devices. Compliance obligations extend to both AI-system providers and deployers, making operational compliance instruments and AI literacy programmes essential for lawful implementation. AI LITERACY: OBLIGATION AND PLANNING: From February 2025, the AI Act mandates AI literacy for all personnel interacting with AI-systems. Training should cover baseline competencies for all staff, advanced modules for specialists, continuous professional development, and integration of ethical, legal, and governance principles. Competency acquisition and updates must be systematically documented to meet institutional and EU compliance standards. OPERATIONAL CHECKLIST FOR THE ADOPTION OF AI POLICY: The checklist has two integrated domains: clinical and technical validation, including evidence-based performance assessment, real-world validation, MDR compliance, GDPR adherence, and post-deployment monitoring; and governance and compliance, covering AI Act conformity, organizational accountability, decision traceability, human oversight, AI literacy, and structured audit and update mechanisms. FUTURE PERSPECTIVES: The checklist methodology offers a scalable, adaptable, regulation-ready framework for AI policy development. By embedding legal compliance, clinical safety, governance, and continuous staff training, it supports sustainable AI integration. Future updates will incorporate regulatory changes, real-world feedback, and impact metrics, enhancing AI's contribution to quality, safety, and equity in patient care.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.