SynthesisFrontiers in psychology2022
Inclusion of Clinicians in the Development and Evaluation of Clinical Artificial Intelligence Tools: A Systematic Literature Review.
Synthesis in Frontiers in psychology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Electronic health record-based prediction models for in-hospital adverse drug event diagnosis or prognosis: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023Pooled it
- Artificial Intelligence as an Add-On Instrument in Fetal Ultrasound; Sonographers' and Obstetricians' Expectations.Prenatal diagnosis · 2026Article
- Clinician Participation in Innovation Labs at University Hospitals: Mixed Methods Study.Journal of medical Internet research · 2026Article
- 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
- Leveraging human-centered AI for clinical decision-making: a transparent, accurate rule extractor using non-dominated sorting genetic algorithm.BMC medical informatics and decision making · 2026Article
- Recent advances in artificial intelligence across interventional pulmonology: a narrative review.Journal of thoracic disease · 2026Review
- Artificial intelligence-generated encounter summaries: early insights from ambulatory clinicians at a large academic health system.JAMIA open · 2025Article
- Implementation of Passive Deterioration Index Alerts in an Intermediate Care Unit: A Failed Early Warning System Strategy.Applied clinical informatics · 2025Article
- Artificial Intelligence in Primary Care: Support or Additional Burden on Physicians' Healthcare Work?-A Qualitative Study.Clinics and practice · 2025Article
- Integrating equity, diversity, and inclusion throughout the lifecycle of artificial intelligence for healthcare: a scoping review.PLOS digital health · 2025Article
- Clinicians must participate in the development of multimodal AI.EClinicalMedicine · 2025Review
- Barriers to and facilitators of clinician acceptance and use of artificial intelligence in healthcare settings: a scoping review.BMJ open · 2025Article
- Future Use of AI in Diagnostic Medicine: 2-Wave Cross-Sectional Survey Study.Journal of medical Internet research · 2025Article
- Benefits and Risks of AI in Health Care: Narrative Review.Interactive journal of medical research · 2024Review
- Review
- Moving beyond Technical Issues to Stakeholder Involvement: Key Areas for Consideration in the Development of Human-Centred and Trusted AI in Healthcare.Asian bioethics review · 2024Article
- Sketching AI Concepts with Capabilities and Examples: AI Innovation in the Intensive Care Unit.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2024Article
- The ability of artificial intelligence tools to formulate orthopaedic clinical decisions in comparison to human clinicians: An analysis of ChatGPT 3.5, ChatGPT 4, and Bard.Journal of orthopaedics · 2024Article
- Automatization of CT Annotation: Combining AI Efficiency with Expert Precision.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial intelligence research in radiation oncology: a practical guide for the clinician on concepts and methods.BJR open · 2024Review
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
The application of machine learning (ML) and artificial intelligence (AI) in healthcare domains has received much attention in recent years, yet significant questions remain about how these new tools integrate into frontline user workflow, and how their design will impact implementation. Lack of acceptance among clinicians is a major barrier to the translation of healthcare innovations into clinical practice. In this systematic review, we examine when and how clinicians are consulted about their needs and desires for clinical AI tools. Forty-five articles met criteria for inclusion, of which 24 were considered design studies. The design studies used a variety of methods to solicit and gather user feedback, with interviews, surveys, and user evaluations. Our findings show that tool designers consult clinicians at various but inconsistent points during the design process, and most typically at later stages in the design cycle (82%, 19/24 design studies). We also observed a smaller amount of studies adopting a human-centered approach and where clinician input was solicited throughout the design process (22%, 5/24). A third (15/45) of all studies reported on clinician trust in clinical AI algorithms and tools. The surveyed articles did not universally report validation against the "gold standard" of clinical expertise or provide detailed descriptions of the algorithms or computational methods used in their work. To realize the full potential of AI tools within healthcare settings, our review suggests there are opportunities to more thoroughly integrate frontline users' needs and feedback in the design process.
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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.