Promoting AI Competencies for Medical Students: A Scoping Review on Frameworks, Programs, and Tools (2407.18939v1)

Abstract: As more clinical workflows continue to be augmented by AI, AI literacy among physicians will become a critical requirement for ensuring safe and ethical AI-enabled patient care. Despite the evolving importance of AI in healthcare, the extent to which it has been adopted into traditional and often-overloaded medical curricula is currently unknown. In a scoping review of 1,699 articles published between January 2016 and June 2024, we identified 18 studies which propose guiding frameworks, and 11 studies documenting real-world instruction, centered around the integration of AI into medical education. We found that comprehensive guidelines will require greater clinical relevance and personalization to suit medical student interests and career trajectories. Current efforts highlight discrepancies in the teaching guidelines, emphasizing AI evaluation and ethics over technical topics such as data science and coding. Additionally, we identified several challenges associated with integrating AI training into the medical education program, including a lack of guidelines to define medical students AI literacy, a perceived lack of proven clinical value, and a scarcity of qualified instructors. With this knowledge, we propose an AI literacy framework to define competencies for medical students. To prioritize relevant and personalized AI education, we categorize literacy into four dimensions: Foundational, Practical, Experimental, and Ethical, with tailored learning objectives to the pre-clinical, clinical, and clinical research stages of medical education. This review provides a road map for developing practical and relevant education strategies for building an AI-competent healthcare workforce.

AI Competencies in Medical Education: Scoping Review Analysis

The research paper titled "Promoting AI Competencies for Medical Students: A Scoping Review on Frameworks, Programs, and Tools" provides an extensive examination of the integration of AI education into medical curricula. The paper identifies the current state of AI literacy among medical students and proposes a comprehensive framework to address existing gaps. Through analyzing theoretical guidelines and practical educational efforts, the paper reveals significant insights into the current landscape and potential future directions of AI education in medicine.

Core Findings

The paper meticulously reviews 29 studies, categorizing them into two primary research questions. The first question assesses the existing theoretical proposals and guidelines for AI education in medical training. The second question investigates practical teaching methods and courses being implemented. The nuanced analysis reveals a disparity between the theoretical emphasis on AI ethics and practical application versus the more technical and data-centric focus evident in current educational programs.

Discrepancies in Educational Emphasis

A major finding discussed in the paper is the discrepancy between proposed theoretical guidelines and the focus of existing educational programs. Theoretical frameworks predominantly highlight ethical considerations and the application of AI tools in clinical settings. However, current programs often emphasize technical knowledge in statistics, data science, and coding. This misalignment could hinder the practical integration of AI in clinical practice, as many medical students may not directly benefit from intensive technical training that is less applicable in everyday clinical contexts.

Challenges in Curriculum Integration

The research outlines several challenges in incorporating AI training into medical education. Among the key obstacles are the congested nature of medical curricula, the evolving nature of AI technologies, and the scarcity of qualified instructors with expertise spanning both medicine and AI. These barriers necessitate strategic curriculum development that leverages interdisciplinary teaching and adaptable learning modules that can quickly integrate advancements in AI.

Proposed AI Literacy Framework

To bridge the identified gaps, the authors propose an AI literacy framework comprising four dimensions: Foundational, Practical, Experimental, and Ethical AI competencies. This framework aims to tailor AI education to medical students' varying stages, from pre-clinical to clinical research. By emphasizing the clinical relevance and ethical use of AI, the framework seeks to cultivate medical professionals capable of leveraging AI technologies effectively and responsibly in their practice.

Educational Tools and Future Directions

The paper points out the potential of online educational tools to democratize AI learning, yet notes their current limitations in catering specifically to medical contexts. Future developments could include creating specialized online platforms that address the unique needs of medical students, ensuring accessibility and scalability of AI education.

Implications for Practice and Research

The implications of this paper are multifaceted. Practically, the paper suggests actionable pathways for integrating AI literacy into medical education, emphasizing the need for tailored, clinically relevant educational content. Theoretically, it opens up avenues for further research into interdisciplinary teaching methods and the ongoing evolution of AI tools in clinical practice.

In conclusion, the paper makes a substantial contribution to understanding the current state and future direction of AI education within medical training. It highlights the importance of aligning educational efforts with practical application and ethical standards, ultimately ensuring that future physicians are competent in using AI technologies to enhance patient care. The insights offered in this scoping review could inform policymakers, educators, and researchers in developing robust strategies to improve AI literacy among medical professionals.