ReviewPatterns (New York, N.Y.)2023
A normative framework for artificial intelligence as a sociotechnical system in healthcare.
Review in Patterns (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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
17 citing papers in PubMed.
- When is AI "just another innovation"? A comparative conceptual analysis of artificial intelligence and evidence-based practice implementation.Implementation science communications · 2026Article
- Sociotechnical Misalignments in Hospital AI System Implementation: Qualitative Case Study.Journal of medical Internet research · 2026Article
- Patients' and Providers' Attitudes Toward Artificial Intelligence and Electronic Health Record Use in Deep Phenotyping and Rare-Disease Screening: An Empty Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- A Futures Framework for Clinical AI Governance: Anticipating Emerging Risks, Shifting Roles, and Regulatory Challenges.Journal of medical Internet research · 2026Article
- Five views on the foundations of data science.Patterns (New York, N.Y.) · 2026Article
- Clinical learning experiences and artificial intelligence-related anxiety among midwifery students: a cross-sectional study.BMC medical education · 2026Article
- Article
- Governance for safe and responsible AI in healthcare organisations: a scoping review of frameworks.NPJ digital medicine · 2026Article
- Integrating Generative AI Into Patient-Centered Clinical Decision Support: Viewpoint on Research and Practice Considerations.Journal of medical Internet research · 2026Article
- Aligning Artificial Intelligence Prediction Targets with Clinical Workflows Using Human Centered Design Methods.medRxiv : the preprint server for health sciences · 2026Article
- A scoping review of artificial intelligence as a medical device for ophthalmic image analysis in Europe, Australia and America.NPJ digital medicine · 2025Article
- Can an accurate model be bad?Patterns (New York, N.Y.) · 2025Article
- Disclosure as Absolution in Medicine: Disentangling Autonomy from Beneficence and Justice in Artificial Intelligence.The American journal of bioethics : AJOB · 2025Article
- Readiness Evaluation for AI-Mental Health Deployment and Implementation (READI): A Review and Proposed Framework.Technology, mind, and behavior · 2025Article
- Explaining decisions without explainability? Artificial intelligence and medicolegal accountability.Future healthcare journal · 2024Article
- Information, collaboration, regulation: Physician and AI researcher views on ethical considerations in clinical AI integration.Big data & societyArticle
- What matters in digital health technology adoption for mental healthcare? - A multistakeholder qualitative study.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) tools are of great interest to healthcare organizations for their potential to improve patient care, yet their translation into clinical settings remains inconsistent. One of the reasons for this gap is that good technical performance does not inevitably result in patient benefit. We advocate for a conceptual shift wherein AI tools are seen as components of an intervention ensemble. The intervention ensemble describes the constellation of practices that, together, bring about benefit to patients or health systems. Shifting from a narrow focus on the tool itself toward the intervention ensemble prioritizes a "sociotechnical" vision for translation of AI that values all components of use that support beneficial patient outcomes. The intervention ensemble approach can be used for regulation, institutional oversight, and for AI adopters to responsibly and ethically appraise, evaluate, and use AI tools.
Identifiers
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.