[Paper Review] Promoting AI Competencies for Medical Students: A Scoping Review on Frameworks, Programs, and Tools

Yingbo Ma, Yukyeong Song+13 more|arXiv (Cornell University)|Jul 10, 2024

Artificial Intelligence in Healthcare and Education10 citations

TL;DR

A scoping review of 1,699 articles (2016–2024) identifies 18 AI education frameworks and 11 instruction programs for medical students, and proposes an AI literacy framework with four dimensions to tailor education across medical training stages.

ABSTRACT

As more clinical workflows continue to be augmented by artificial intelligence (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.

Motivation & Objective

• Motivate the need for AI literacy among physicians as AI-driven care grows.
• Assess how AI is currently integrated into medical education across curricula and programs.
• Synthesize existing frameworks and instructional examples to guide future curricula.
• Highlight gaps and challenges in defining AI literacy and delivering effective instruction.

Proposed method

• Conducted a scoping review of 1,699 articles published between January 2016 and June 2024.
• Identified studies proposing guiding frameworks (n=18) and studies documenting real-world instruction (n=11).
• Analyzed content to assess clinical relevance, personalization, and alignment with medical student trajectories.
• Identified discrepancies in teaching guidelines, with emphasis on AI evaluation and ethics over technical topics.
• Developed an AI literacy framework with four dimensions to tailor learning objectives to educational stages.

Experimental results

Research questions

• RQ1 What AI education frameworks exist for medical students and how are they structured?
• RQ2 What kinds of real-world AI instruction are described in medical education literature?
• RQ3 What are the common gaps, challenges, and ambiguities in AI literacy and curriculum design for medical students?
• RQ4 How can AI literacy be defined and operationalized to fit different stages of medical training?

Key findings

• Eighteen studies propose guiding frameworks for AI education in medical contexts.
• Eleven studies document actual AI instruction implemented in medical education settings.
• Teaching guidelines show a bias toward AI evaluation and ethics over technical topics like data science and coding.
• There are notable discrepancies in guidelines and a perceived lack of proven clinical value for AI training.
• A lack of standardized definitions for AI literacy and a shortage of qualified instructors are key barriers.
• An AI literacy framework with four dimensions (Foundational, Practical, Experimental, Ethical) is proposed to tailor objectives across pre-clinical, clinical, and clinical research stages.