Evidence map›Paper›PMID 41327942›Full record

ReviewMedical education online2025

Twelve tips for developing and implementing AI curriculum for undergraduate medical education.

Do-Hwan Kim, Ye Ji Kang, Young-Mee Lee

Abstract readReview
In one paragraph

Review in Medical education online, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. AI Competency: Current State and Challenges.JMIR medical education · 2026
    Article
  7. Article
  8. Article
  9. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Do-Hwan KimDepartment of Medical Education, Korea University College of Medicine, Seoul, Korea.ORCID 0000-0003-4137-7130
Ye Ji KangDepartment of Medical Education, Inha University College of Medicine, Incheon, Korea.ORCID 0000-0003-1711-2394
Young-Mee LeeDepartment of Medical Education, Korea University College of Medicine & the National Academy of Medicine, Seoul, Korea.ORCID 0000-0002-4685-9465

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid evolution of artificial intelligence (AI) and its growing role in clinical settings have made AI education a priority in undergraduate medical education. To support this, AI curricula must align with existing medical education frameworks while addressing AI's distinctive characteristics. This article outlines twelve actionable tips to guide the development and implementation of such curricula. These include defining the purpose and scope of AI education within the broader context of existing competency frameworks and digital health. The curriculum should be structured to allow for progressive deepening and integration of content, prioritizing key elements. Additionally, sustainable AI education depends on securing institutional resources, providing learners with authentic experiences, and ensuring continuous evaluation and improvement of the curriculum. Together, these approaches aim to help medical schools prepare students to practice effectively in a future where AI is a core component of medical practice.

Indexed as

Artificial IntelligenceCurriculumEducation, Medical, UndergraduateHumansArtificial intelligencecompetency-based educationcurriculum development

Identifiers

PMID41327942
PMCPMC12673982

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.