It Takes More Than Enthusiasm
The Missing Infrastructure to Unlock AI’s Potential in Medical Education
Bibliographic Data
| ID | 21612840 |
|---|---|
| Authors | Laurah Turner (0000-0002-4567-1313, ORCID, corresponding author), Christine Zhou (0000-0002-2143-1972, ORCID), Jesse Burk-Rafel (0000-0003-3785-2154, ORCID) |
| Year | 2025 |
| Volume | 100 |
| Issue | 9S |
| Pages | S34-S38 |
| Publication date | 2025-09-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Academic Medicine (JOURNAL) |
| Journal identifiers | ISSN: 1040-2446 • E-ISSN: 1938-808X |
| Publisher | Oxford University Press (OUP) (PUBLISHER) |
| DOI | 10.1097/acm.0000000000006104 |
| PMID | 40456123 |
| OpenAlex | W4410943182 |
| Language | EN |
| Citations received | 2 |
| References cited | 12 |
Generative artificial intelligence (AI), including large language models (LLMs), is rapidly transforming health care delivery, yet medical education remains unprepared to harness its potential or mitigate its risks. While AI holds immense potential to enhance medical education, unguided adoption of these tools without proper educational frameworks risks undermining learners’ clinical reasoning development and professional growth, as was seen with the electronic health record. In this commentary, the authors argue that the primary barrier to effective AI integration in medical education is not technological sophistication, but rather 3 critical infrastructure deficiencies: institutional implementation structures, sustainable funding mechanisms, and rigorous research methodologies. The authors propose establishing dedicated educational informatics teams with executive authority, creating targeted funding streams modeled after clinical research investments, and developing rigorous assessment frameworks with clear benchmarks for educational outcomes. Without these foundational elements, AI integration risks exacerbating inequities between institutions, potentially compromising physician development, and ultimately failing to improve patient care. Recommendations developed at a Macy Foundation conference on AI and Medical Education provide a roadmap for addressing these challenges, but significant infrastructural support is required to realize their potential. The authors argue that failure to address these structural gaps would perpetuate a cycle of innovation without implementation, a challenge that has plagued medical education for decades. In an era when AI is reshaping clinical practice daily, trainees cannot afford another well-intentioned but under-resourced educational transformation. Transformative educational change demands more than enthusiasm—it requires institutional commitment, significant investment, and methodological rigor commensurate with the high stakes of physician preparation
Curriculum · Engineering ethics · Enthusiasm · Health care · Pedagogy · Political science · Public relations · Sociology · Sophistication · Transformative learning · Artificial Intelligence in Healthcare and Education · Engineering · Health and Medical Research Impacts · Innovations in Medical Education · Medicine · Psychology
| Unique citing works | 2 |
|---|---|
| Citations per year | 2 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 2 |