SynthesisBMC medical education2025
Content and structural needs assessment for an artificial intelligence education mobile app in healthcare: a mixed methods study.
Synthesis in BMC medical education, 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
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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.
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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.
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Who cites it
3 citing papers in PubMed.
- Attitudes and Usage of ChatGPT Among Medical and Paramedical Students in Iran: A Cross-Sectional Study.Health science reports · 2026Article
- An artificial intelligence application framework for nursing education: a scoping review based on the KAS model.BMC medical education · 2026Article
- Knowledge, Attitude, and Practice Toward Diabetic Retinopathy Among People With Diabetes: A Systematic Review and Meta-Analysis.Health science reports · 2026Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aimed to identify and prioritize the core content and structural requirements for developing a high-quality mobile app designed to teach AI concepts and skills in a healthcare context.
methodsA mixed-methods design was employed. First, two systematic reviews were conducted: [1] a review of scholarly articles to extract educational frameworks for AI in healthcare, and [2] a review of 47 AI education apps from three app stores (Google Play, App Store, Café Bazaar), assessed using the Mobile App Rating Scale (MARS). As no healthcare-specific AI education apps were found during the search, general-purpose AI learning apps were included, which constitutes a limitation in terms of domain specificity. Based on these insights, a preliminary content framework was developed and validated by 12 experts in medical informatics and health information management. Subsequently, a structural needs assessment was carried out with 97 healthcare students using custom-designed questionnaires. Open-ended responses were analyzed using Braun and Clarke's thematic analysis method.
resultsThe systematic review of 37 articles revealed 10 key domains essential for AI education in healthcare, including foundational knowledge, data science, practical clinical applications, ethics, and communication. The app review showed a mean MARS quality score of 2.92 out of 5, highlighting significant deficiencies in content coherence, interactivity, and privacy implementation. Expert validation confirmed all proposed domains, and thematic analysis of expert feedback led to the inclusion of an additional domain: Practical Tools and Platforms. Healthcare students strongly favored features such as interactive learning, offline functionality, and personalized learning paths (mean scores > 4.76/5), with no significant differences across gender or field of study.
conclusionThis study presents a validated, evidence-based framework for developing a healthcare-focused AI education app. The finalized structure includes 11 content domains and 20 prioritized structural features aimed at promoting practical, ethical, and engaging learning experiences. The findings underscore the urgent need for structured, user-centered digital tools to prepare healthcare students and professionals for the responsible integration of AI into clinical practice.
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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.