ReviewHealthcare (Basel, Switzerland)2025
Bridging the Gap: From AI Success in Clinical Trials to Real-World Healthcare Implementation-A Narrative Review.
Review in Healthcare (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 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
48 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- Adoption of artificial intelligence in primary health care: systematic synthesis of stakeholder perspectives.BMC primary care · 2026Pooled it
- Artificial intelligence in mental health care: a scoping review of reviews.Frontiers in psychiatry · 2026Pooled it
- Systematic Review of Artificial Intelligence Applications in Clinical Trials for Central Nervous System Injuries.Current neuropharmacology · 2026Pooled it
- Ethical challenges in the algorithmic era: a systematic rapid review of risk insights and governance pathways for nursing predictive analytics and early warning systems.BMC medical ethics · 2025Pooled it
- Integrative review of artificial intelligence applications in nursing: education, clinical practice, workload management, and professional perceptions.Frontiers in public health · 2025Pooled it
- Artificial intelligence in nursing: a systematic review of attitudes, literacy, readiness, and adoption intentions among nursing students and practicing nurses.Frontiers in digital health · 2025Pooled it
- Economic, ethical, and regulatory dimensions of artificial intelligence in healthcare: an integrative review.Frontiers in public health · 2025Pooled it
- Beyond the algorithm: why oncology nursing in Asia-Pacific needs evidence-based AI evaluation.Asia-Pacific journal of oncology nursing · 2026Article
- The AI Adaptation Paradox: Navigating Professional Relevance and Sustainable Integration in Nursing.Nursing inquiry · 2026Article
- A five-phase evaluation framework for diagnostic and predictive medical artificial intelligence.NPJ digital medicine · 2026Article
- Systems Bioengineering of Septic Shock Metabolism: Citrulline, β-Hydroxybutyrate and Plasma Biomarker-Based Phenotyping.Biomolecules · 2026Review
- Use of Computer Vision with Conventional Video Recordings of Gait in Older Adults: A Scoping Review.Healthcare (Basel, Switzerland) · 2026Review
- A lifecycle governance and learning health system framework for trustworthy, generalizable, and sustainable human-ai partnership in clinical practice: Lessons from the asthma-guidance and prediction system (A-GPS).Journal of the National Medical Association · 2026Review
- Closing the Translational Gap: Closed-Loop AI Discovery Frameworks for Experimental Validation and Clinical Implementation in Cancer Therapeutics.Cancer medicine · 2026Review
- Mapping the research landscape of artificial intelligence in heart failure: a bibliometric analysis.Annals of medicine and surgery (2012) · 2026Article
- 1,357 AI medical devices cleared, 3 actually tested on patient outcomes.PLOS digital health · 2026Review
- Clinical Performance and Implementation of AI-Enabled Paediatric Ophthalmic Screening, Triage, Diagnosis, and Surveillance in Primary, Community, and Referral-Linked Pathways: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Clinical AI Beyond Development: A Scoping Review of Deployment-Related Robustness, Algorithmovigilance, and Lifecycle Oversight.Healthcare (Basel, Switzerland) · 2026Review
- Benchmarking large language models against practicing clinicians on psychopathological assessment.NPJ digital medicine · 2026Article
- MEL-IA: An Interoperable AI System for Multimodal Skin Lesion Classification in Hospital Settings.Journal of medical systems · 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
7 authors.
Funding
Abstract
backgroundArtificial intelligence (AI) has demonstrated remarkable diagnostic accuracy in controlled clinical trials, sometimes rivaling or even surpassing experienced clinicians. However, AI's real-world effectiveness is frequently diminished when applied to diverse clinical settings, owing to methodological shortcomings, limited multicenter studies, and insufficient real-world validations.
objectiveThis narrative review critically examines the discrepancy between AI's robust performance in clinical trials and its inconsistent real-world implementation. Our goal is to synthesize methodological, ethical, and operational challenges impeding AI integration and propose a comprehensive framework to bridge this gap.
methodsWe conducted a thematic synthesis of peer-reviewed studies from the PubMed, IEEE Xplore, and Scopus databases, targeting studies from 2014 to 2024. Included studies addressed diagnostic, therapeutic, or operational AI applications and related implementation challenges in healthcare. Non-peer-reviewed articles and studies without rigorous analysis were excluded.
resultsOur synthesis identified key barriers to AI's real-world deployment, including algorithmic bias from homogeneous datasets, workflow misalignment, increased clinician workload, and ethical concerns surrounding transparency, accountability, and data privacy. Additionally, scalability remains a challenge due to interoperability issues, insufficient methodological rigor, and inconsistent reporting standards. To address these challenges, we introduce the AI Healthcare Integration Framework (AI-HIF), a structured model incorporating theoretical and operational strategies for responsible AI implementation in healthcare.
conclusionsTranslating AI from controlled environments to real-world clinical practice necessitates a multifaceted, interdisciplinary approach. Future research should prioritize large-scale pragmatic trials and observational studies to empirically validate the proposed AI Healthcare Integration Framework (AI-HIF) in diverse, real-world healthcare contexts.
Indexed as
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.