SynthesisBMC medical education2026
Assessing the effectiveness of artificial intelligence education and training for healthcare workers: a systematic review.
Synthesis in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Artificial intelligence literacy and associations with thriving at work among nurses in Anhui Province, China: a latent profile analysis.BMC nursing · 2026Article
- Artificial Intelligence in Orthopaedic Education Across the Career Continuum: Moving from Benchmark Performance to Continuing Professional Development.Journal of CME · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) is increasingly integrated into healthcare, yet upskilling the health workforce remains a challenge. We addressed the research question: What evidence exists on the effectiveness of AI education and training programs in improving AI literacy among healthcare workers?
methodsFollowing PRISMA guidelines and PROSPERO registration, five databases (PubMed, Scopus, CINAHL, Embase, ERIC) were searched on 20 August 2024, focusing on studies with an intervention of AI training or education for the healthcare workforce, in any study design that reported an evaluation.
results27 studies were included. Programs improved AI literacy outcomes mapped to levels 1–3 of the Kirkpatrick-Barr training evaluation hierarchy including improved learner reactions, shifts in attitudes and perceptions, enhanced knowledge and skills, and behavior changes. Programs did not map to level 4, where healthcare workers learn to metacognition levels, including organizational change and patient benefit. Programs were short in length (44%), delivered in academic settings (56%), to doctors (44%) or medical students (44%), at entry-to-practice level (56%). Most taught an introduction to AI (67%), with technical AI skills less frequent.
conclusionsThese programs are a promising start but often lack sufficient depth to build advanced competencies. Improving AI literacy in healthcare will require appropriate course design, an evolving understanding of this rapidly changing area, and evaluating learning effectiveness. As the adoption of AI accelerates across healthcare, health systems may seek to standardise and assess the efficacy of these courses.
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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.