ArticleAdvances in health information science and practice2025
Assessing Artificial Intelligence Literacy: What do Health Information Professionals Know about AI?
Article in Advances in health information science and practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Building health-literate artificial intelligence.Nature human behaviour · 2026Review
- Stakeholder Perspectives on Health System Readiness for Integrating Artificial Intelligence into Public Health in Saudi Arabia.Healthcare (Basel, Switzerland) · 2026Article
- Artificial intelligence in digital mental health technology: Considerations for regulation.Digital healthReview
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
No grant is acknowledged in the PubMed record.
Abstract
Background: Previous AI literacy studies have been limited to clinical students and professionals and included subjective reporting. This survey study explored the extent of AI knowledge in health information professionals with subjective and objective questions. The objective of the study was to inform the International Federation of Health Information Management Associations (IFHIMA) and national health information bodies about current AI literacy levels and the education needs of their members. Methods: A descriptive survey was adapted from two validated, previously-published study instruments. Survey data were collected between December 5, 2024, and February 28, 2025 using a self-administered Qualtrics (Seattle, WA) online survey link distributed by email to IFHIMA members. The survey link was also distributed on the LinkedIn professional networking platform (LinkedIn Corp; Sunnyvale, CA) by multiple IFHIMA members. Results were analyzed using Chi-square, ANOVA, and Tukey HSD post-hoc tests to assess the associations between the categorical response variables and the subjective survey question measures. Results: A total of 176 participants began the survey. Data were cleaned to exclude 48 incomplete responses, leaving 128 complete and valid responses for analysis. AI knowledge varied by demographics; country of employment or residence and professional association membership were shown to influence familiarity with AI. Many health information professionals reported limited or no AI experience, and those with practical AI experience performed better on foundational AI knowledge questions, suggesting that experiential learning scaffolds AI literacy. Most respondents understood emerging AI-related threats. However, regardless of experiences with everyday AI tools, they struggled with AI modeling and product development. Conclusions: The study results identified a major gap in AI knowledge, and the authors provide input for educators aiming to align educational programs with job market demand by increasing AI knowledge content, addressing gaps through targeted curriculum development and educator training.
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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.