Enterprise AI Analysis
Artificial intelligence in undergraduate medical education: an updated scoping review
This scoping review highlights the dramatic increase in AI's role in undergraduate medical education (UGME), presenting both benefits and challenges. Since the public release of large language models (LLMs) in 2022, AI-related publications have surged, with 52% appearing in the last eight months. Students overwhelmingly desire AI literacy, viewing AI as a supportive tool rather than a replacement for human physicians, though concerns exist regarding critical thinking, ethics, and over-reliance. Current AI applications span basic sciences to clinical skills, from autonomous tutoring to assessment generation. However, a lack of standardized competencies, pedagogical methods, and ethical guidelines, coupled with faculty preparedness gaps and infrastructure costs, hinder effective implementation. The review emphasizes urgent needs for defining AI competencies, developing ethical frameworks, and investing in faculty development, particularly given the global disparities in AI research and adoption.
Executive Impact & Key Metrics
Quantitative insights demonstrating the rapid evolution and adoption of AI in undergraduate medical education, and key readiness indicators.
0
Total Publications Since 2020
0
Publications Post-LLM (2023-2024)
0%
AI Integration Readiness (Students)
Low / None
Formal AI Training for Students & Faculty
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Dramatic Growth in AI Literature
Geographic Distribution of AI Research
Student and Faculty Perceptions of AI
Diverse AI Applications in UGME
Key Challenges to AI Integration in UGME
Critical Unaddressed Areas & Priorities
This highlights the dramatic acceleration of AI research in undergraduate medical education following the release of LLM-based tools, necessitating continuous updates to curriculum and policy.
The majority of research originates from Western European and Others Group (WEOG) and Asia and Pacific Group (APG), with limited cross-regional collaboration, indicating potential for a widening digital divide and emphasizing the need for global collaborative endeavors.
While students are largely positive and eager to integrate AI, both students and faculty share significant concerns about AI literacy, ethical implications, and the potential impact on critical thinking and academic integrity.
AI's practical uses are expanding rapidly across medical education. From interactive anatomy learning and surgical skills training to personalized feedback and exam generation, AI offers powerful tools. LLMs specifically enhance personalized learning, research assistance, and content creation.
Integrating AI into UGME curricula faces significant hurdles, including defining competencies, managing rapid technological evolution, addressing faculty preparedness, and overcoming resource constraints, alongside complex ethical dilemmas.
Despite rapid adoption, crucial areas remain unaddressed, particularly the impact on core medical skills. Establishing clear guidelines and robust faculty training are paramount to ensure responsible and effective AI integration.
0%
of AI-related publications in UGME appeared in 8 months between the last review and current study.
This highlights the dramatic acceleration of AI research in undergraduate medical education following the release of LLM-based tools, necessitating continuous updates to curriculum and policy.
Geographic Distribution of AI Research
WEOG: 53% primary contributions
→
APG: 39%
→
EEG/LAC/AG: 3% each
→
Cross-regional: 9% (2 groups)
→
Cross-regional: 1% (3 groups)
→
Cross-regional: 1 publication (all 5 groups)
The majority of research originates from Western European and Others Group (WEOG) and Asia and Pacific Group (APG), with limited cross-regional collaboration, indicating potential for a widening digital divide and emphasizing the need for global collaborative endeavors.
| Group | Positive Perceptions | Concerns |
|---|---|---|
| Medical Students |
|
|
| Faculty |
|
|
While students are largely positive and eager to integrate AI, both students and faculty share significant concerns about AI literacy, ethical implications, and the potential impact on critical thinking and academic integrity.
Diverse AI Applications in UGME
"AI is being utilized across various stages of UGME – from basic sciences and preclinical years to clinical years – as well as in numerous fields and specialty areas, with applications ranging from autonomous tutoring, self-assessment, and simulation-based learning to assessment generation and grading, clinical assessment, procedural skills evaluation, and predictive analytics."
AI's practical uses are expanding rapidly across medical education. From interactive anatomy learning and surgical skills training to personalized feedback and exam generation, AI offers powerful tools. LLMs specifically enhance personalized learning, research assistance, and content creation.
Key Challenges to AI Integration in UGME
- Lack of standardized definitions for AI and core competencies.
- Absence of formal guidance from oversight bodies.
- Limited curriculum space and rapid technological evolution.
- Challenges for medical educators to teach complex AI concepts.
- Lack of AI knowledge and expertise among faculty; resistance to adopting new technologies.
- Infrastructure, financial resources, and investment barriers.
- Ethical considerations: algorithmic bias, data privacy, hallucinations, lack of transparency, plagiarism risk.
Integrating AI into UGME curricula faces significant hurdles, including defining competencies, managing rapid technological evolution, addressing faculty preparedness, and overcoming resource constraints, alongside complex ethical dilemmas.
Critical Unaddressed Areas & Priorities
- No publications assessed AI's impact on critical thinking or clinical reasoning in medical students.
- Urgent need for defining AI competencies, pedagogical methods, and ethical guidelines.
- Faculty development in AI is vital.
- Further research needed on AI's impact on ethics, empathy, critical thinking, and clinical reasoning.
- Collaborative and international endeavors are essential to address global disparities.
Despite rapid adoption, crucial areas remain unaddressed, particularly the impact on core medical skills. Establishing clear guidelines and robust faculty training are paramount to ensure responsible and effective AI integration.
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Phased Implementation Roadmap
Our strategic roadmap outlines a clear, actionable path to successfully integrate AI into your medical education programs, ensuring long-term benefits and ethical compliance.
Phase 1: Foundation & Strategy (Months 1-3)
Establish consensus on AI competencies and ethical frameworks for UGME. Conduct a comprehensive faculty needs assessment for AI training and identify key areas for development. Initiate pilot programs of AI tools with small student groups to gather initial feedback and assess feasibility. Begin developing flexible, longitudinal AI literacy approaches for integration into existing subjects.
Phase 2: Curriculum Integration & Development (Months 4-9)
Develop and integrate modular AI literacy curriculum across UGME years, using case-based learning and simulation to apply AI in real-world scenarios. Implement AI-focused faculty development programs, including training on AI knowledge, ethical considerations, and pedagogical methods. Create or adapt AI-driven teaching materials, case vignettes, and assessment tools, ensuring human oversight.
Phase 3: Evaluation & Refinement (Months 10-15)
Systematically evaluate the impact of AI interventions on student learning outcomes, critical thinking, clinical reasoning, and empathy using rigorous research methods (e.g., natural experiments). Refine curriculum and faculty development programs based on continuous feedback and assessment data. Develop open-access repositories of best practices for AI tool selection and implementation, fostering collaborative and international efforts to address global disparities.
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