ReviewYearbook of medical informatics2024
Bridging the Gap: Challenges and Strategies for the Implementation of Artificial Intelligence-based Clinical Decision Support Systems in Clinical Practice.
Review in Yearbook of medical informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Clinicians' Attitudes and Perceptions on the Adoption of AI in Mental Health Care: Scoping Review.Journal of medical Internet research · 2026Article
- When is AI "just another innovation"? A comparative conceptual analysis of artificial intelligence and evidence-based practice implementation.Implementation science communications · 2026Article
- Application of Artificial Intelligence for Predicting Sports Injuries and Customizing Personalized Prevention Strategies: A Scoping Review.Bioengineering (Basel, Switzerland) · 2026Review
- Impact of an AI prognostic tool on clinician performance in colorectal liver metastases.NPJ digital medicine · 2026Article
- Review
- Application of deep learning in prognostic prediction of non-small cell lung cancer.Frontiers in cell and developmental biology · 2026Review
- Analyzing and redesigning deployment pathways for internally developed artificial intelligence tools in clinical education and health research.Discover artificial intelligence · 2026Article
- Translational Potential and Explainability of Artificial Intelligence-Based Clinical Decision Support for Adults in Intensive Care: A Scoping Review.Journal of multidisciplinary healthcare · 2026Review
- The anatomy of AI implementation skepticism in Polish healthcare: an explanatory mixed-methods analysis of psychographic barriers among healthcare professionals.Frontiers in public health · 2026Article
- Artificial Intelligence Application in Cornea and External Diseases.Diagnostics (Basel, Switzerland) · 2025Review
- AI in primary care - a general practitioner's bucket list.The European journal of general practice · 2025Article
- Saudi Medical Appointments and Referrals Center (SMARC) Performance Dynamic: A Comparative National Analysis of 2023-2024 Against Baseline Metrics.Healthcare (Basel, Switzerland) · 2025Article
- Improving AI-Based Clinical Decision Support Systems and Their Integration Into Care From the Perspective of Experts: Interview Study Among Different Stakeholders.JMIR medical informatics · 2025Article
- Advancing Clinical Information Systems: Harnessing Telemedicine, Data Science, and AI for Enhanced and More Precise Healthcare Delivery.Yearbook of medical informatics · 2024Review
- Digital Health for Precision Prevention.Yearbook of medical informatics · 2024Article
- Review
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
objectivesDespite the surge in development of artificial intelligence (AI) algorithms to support clinical decision-making, few of these algorithms are used in practice. We reviewed recent literature on clinical deployment of AI-based clinical decision support systems (AI-CDSS), and assessed the maturity of AI-CDSS implementation research. We also aimed to compare and contrast implementation of rule-based CDSS with implementation of AI-CDSS, and to give recommendations for future research in this area.
methodsWe searched PubMed and Scopus for publications in 2022 and 2023 that focused on AI and/or CDSS, health care, and implementation research, and extracted: clinical setting; clinical task; translational research phase; study design; participants; implementation theory, model or framework used; and key findings.
resultsWe selected and described a total of 31 recent papers addressing implementation of AI-CDSS in clinical practice, categorised into four groups: (i) Implementation theories, frameworks, and models (4 papers); (ii) Stakeholder perspectives (22 papers); (iii) Implementation feasibility (three papers); and (iv) Technical infrastructure (2 papers). Stakeholders saw potential benefits of AI-CDSS, but emphasized the need for a strong evidence base and indicated that systems should fit into clinical workflows. There were clear similarities with rule-based CDSS, but also differences with respect to trust and transparency, knowledge, intellectual property, and regulation.
conclusionsThe field of AI-CDSS implementation research is still in its infancy. It can be strengthened by grounding studies in established theories, models and frameworks from implementation science, focusing on the perspectives of stakeholder groups other than healthcare professionals, conducting more real-world implementation feasibility studies, and through development of reusable technical infrastructure that facilitates rapid deployment of AI-CDSS in clinical practice.
Indexed as
Identifiers
What Socratic 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 Socratic graph.