Medical education currently faces a transformative period marked by rapidly changing clinical requirements and groundbreaking technological advancements. The introduction of artificial intelligence (AI) into this field has given rise to adaptive learning algorithms that tailor educational content in real time to each learner's needs. Alongside AI, immersive technologies, including virtual, augmented, and mixed reality, offer exceptionally realistic simulations of clinical environments. Despite these significant innovations, a lack of interconnected deployment remains, limiting the potential impact and transformative power of these technologies in the medical education landscape. Key challenges, including the absence of seamless systems integration, scalability issues, interoperability hurdles, and inadequate evaluation metrics, continue to hinder the development of effective educational tools and programs.
This Research Topic aims to catalyze advancements in medical education by promoting research that combines the strengths of AI and immersive virtual technologies into cohesive, scalable frameworks. The objective is to integrate AI-driven analytics with immersive learning environments to create dynamic and adaptive educational experiences. Contributions are encouraged to address the development of foundational algorithms, integration of hardware and software, practical implementation in various educational settings, and evaluation of impacts on education, organizational practices, or patient care. Topics related to cost-effectiveness, user acceptance, curriculum transformation, faculty development, and policy considerations will also be considered.
To gather further insights within the boundaries of integrating AI and immersive technologies in medical education, we welcome articles addressing (but not limited to) the following themes:
- Real-time AI-driven scenario adaptation, - Design and integration of immersive environments, - Interoperability, data standards, and security, - Scalable deployment in diverse educational settings, - Curriculum integration and faculty development, - Rigorous evaluation of learner outcomes and long-term retention, - Cost-effectiveness and return on investment, - User acceptance, usability, and engagement metrics, - Ethical, legal, and data privacy considerations.
We welcome submissions of Original Research, Systematic Reviews, Methods articles, Technical Reports, Case Studies, and all article types welcomed by Frontiers in Digital Health. By fostering this collaborative inquiry, this Research Topic aims to remove adoption barriers and initiate widespread use of these innovations in health professional education.
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Case Report
Classification
Clinical Trial
Community Case Study
Curriculum, Instruction, and Pedagogy
Data Report
Editorial
FAIR² Data
Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.
Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.