ReviewNPJ digital medicine2024
To warrant clinical adoption AI models require a multi-faceted implementation evaluation.
Review in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 7 of them syntheses 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
38 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- AI-Based Clinical Decision Support Systems in Primary Care: Systematic Review of Clinical Implementation.Journal of medical Internet research · 2026Pooled it
- Research on patient-facing chatbots based on large language models in the care of older people: a living systematic review.European geriatric medicine · 2026Pooled it
- A Systematic Review and Meta-analysis of Externally Validated Epic Clinical Decision Support Tools.Journal of general internal medicine · 2026Pooled it
- Magnetic resonance imaging and the evaluation of vestibular schwannomas: a systematic review.Neuroradiology · 2026Pooled it
- Task-Technology Fit of Artificial Intelligence-based clinical decision support systems: a review of qualitative studies.BMC medical informatics and decision making · 2025Pooled it
- Operationalization of Artificial Intelligence Applications in the Intensive Care Unit: A Systematic Review.JAMA network open · 2025Pooled it
- From bytes to bedside: a systematic review on the use and readiness of artificial intelligence in the neonatal and pediatric intensive care unit.Intensive care medicine · 2024Pooled it
- An eyecare foundation model for clinical assistance: a randomized controlled trial.Nature medicine · 2025Trial
- Beyond the algorithm: why oncology nursing in Asia-Pacific needs evidence-based AI evaluation.Asia-Pacific journal of oncology nursing · 2026Article
- When Do Physicians Trust AI? The Role of Clinical Context in Decision Support: SMART vignette study.Research square · 2026Article
- AI agents in cancer imaging: Concepts, advances, and clinical perspectives.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Artificial Intelligence Readiness in Clinical Trial Operations: A Narrative Review and Site-Level Governance Framework.Healthcare (Basel, Switzerland) · 2026Review
- Reply to: Clinician engagement modifies effectiveness of AI enabled ECG screening.NPJ digital medicine · 2026Article
- Advancing Human-Centered AI in Clinical Decision Support: Sociocognitive Human-in-the-Loop Study in HIV Care.Journal of medical Internet research · 2026Article
- Artificial Intelligence and Clinician Burnout in the United States: A Narrative Review.Cureus · 2026Review
- Stakeholder Perspectives of Implementation Barriers of Artificial Intelligence in Eye Care: A qualitative framework-based study.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2026Article
- An evaluation of artificial intelligence assisted prostate biopsy reporting in the Articulate Pro study.NPJ digital medicine · 2026Article
- Moving beyond the benchmarks: Five foundational principles for meaningful AI evaluation in healthcare.PLOS digital health · 2026Article
- Reinforcement learning based automated anesthesia system for gastrointestinal endoscopy with a multicenter randomized trial.NPJ digital medicine · 2026Article
- Determinants of the Public's Behavioral Intention to Adopt AI-Assisted Lung Cancer Screening: An Extended UTAUT Model Integrating Trust and Risk.Healthcare (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite artificial intelligence (AI) technology progresses at unprecedented rate, our ability to translate these advancements into clinical value and adoption at the bedside remains comparatively limited. This paper reviews the current use of implementation outcomes in randomized controlled trials evaluating AI-based clinical decision support and found limited adoption. To advance trust and clinical adoption of AI, there is a need to bridge the gap between traditional quantitative metrics and implementation outcomes to better grasp the reasons behind the success or failure of AI systems and improve their translation into clinical value.
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.