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Objective <p>This 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.</p> Methods <p>A mixed-methods design was employed. First, two systematic reviews were conducted: [<CitationRef CitationID="CR1">1</CitationRef>] a review of scholarly articles to extract educational frameworks for AI in healthcare, and [<CitationRef CitationID="CR2">2</CitationRef>] 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.</p> Results <p>The 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: <i>Practical Tools and Platforms</i>. Healthcare students strongly favored features such as interactive learning, offline functionality, and personalized learning paths (mean scores &gt; 4.76/5), with no significant differences across gender or field of study.</p> Conclusion <p>This 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.</p>

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Content and structural needs assessment for an artificial intelligence education mobile app in healthcare: a mixed methods study

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月29日

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  • Seyyedeh Fatemeh Mousavi Baigi,
  • Reyhane Norouzi Aval,
  • Masoumeh Sarbaz,
  • Seyyed Mohammad Tabatabaei,
  • Khalil Kimiafar

摘要

Objective

This 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.

Methods

A 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.

Results

The 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.

Conclusion

This 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.