Promoting AI Competencies for Medical Students: A Scoping Review on Frameworks, Programs, and Tools
Project Overview
The document explores the integration of generative AI in medical education, underscoring the necessity for medical students to acquire AI competencies to enhance patient care effectively and ethically. It identifies significant gaps in current medical curricula regarding AI education and proposes a comprehensive framework for AI literacy that encompasses four dimensions: Foundational, Practical, Experimental, and Ethical, each designed for various stages of medical training. Additionally, the document reviews existing studies on the implementation of AI education programs, analyzing their structures, outcomes, and limitations. Key findings suggest that while there are promising initiatives, barriers to effective implementation remain, necessitating a concerted effort to overcome these challenges and foster a deeper understanding of AI technologies in future healthcare professionals. Overall, the document advocates for a systematic approach to embedding AI literacy in medical education to prepare students for the evolving landscape of healthcare.
Key Applications
AI Literacy and Training Programs for Medical Students
Context: Medical education for students at various stages, including pre-clinical, clinical, and research, through elective courses, workshops, and informal training.
Implementation: A comprehensive scoping review and analysis of existing studies and programs detailing various AI education initiatives, including their structures, methodologies, and outcomes.
Outcomes: Enhanced understanding of AI applications and ethical considerations, increased knowledge and skills in AI, high satisfaction ratings among students, and greater confidence and interest in AI among medical students.
Challenges: Integration into overcrowded curricula, lack of qualified instructors, varying student interest, small sample sizes, reliance on self-reported metrics, and lack of control groups.
Implementation Barriers
Structural
Integrating AI into an already overloaded medical curriculum is difficult.
Proposed Solutions: Propose flexible and relevant AI education strategies, integrate clinical case studies.
Instructor-related
A lack of qualified educators who can effectively teach AI principles within medical contexts.
Proposed Solutions: Encourage collaboration among university departments to share expertise.
Content-related
Need for clarity on what AI competencies are essential for medical students.
Proposed Solutions: Develop clear guidelines and frameworks tailored to medical students' needs.
Project Team
Yingbo Ma
Researcher
Yukyeong Song
Researcher
Jeremy A. Balch
Researcher
Yuanfang Ren
Researcher
Divya Vellanki
Researcher
Zhenhong Hu
Researcher
Meghan Brennan
Researcher
Suraj Kolla
Researcher
Ziyuan Guan
Researcher
Brooke Armfield
Researcher
Tezcan Ozrazgat-Baslanti
Researcher
Parisa Rashidi
Researcher
Tyler J. Loftus
Researcher
Azra Bihorac
Researcher
Benjamin Shickel
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Yingbo Ma, Yukyeong Song, Jeremy A. Balch, Yuanfang Ren, Divya Vellanki, Zhenhong Hu, Meghan Brennan, Suraj Kolla, Ziyuan Guan, Brooke Armfield, Tezcan Ozrazgat-Baslanti, Parisa Rashidi, Tyler J. Loftus, Azra Bihorac, Benjamin Shickel
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai