Evidence map›Paper›PMID 40718215›Full record

ArticleCureus2025

Challenges in the Rapid and Responsible Integration of Generative Artificial Intelligence (AI) Into a New Medical School Curriculum.

Gabrielle Rueff, Paul Monaco, Antonio E Rusinol, Mark Hernandez

Abstract read
In one paragraph

Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

4 authors.

Gabrielle RueffMedical Education, East Tennessee State University Quillen College of Medicine, Johnson City, USA.
Paul MonacoMedical Education, East Tennessee State University Quillen College of Medicine, Johnson City, USA.
Antonio E RusinolMedical Education, East Tennessee State University Quillen College of Medicine, Johnson City, USA.
Mark HernandezMedical Education, East Tennessee State University Quillen College of Medicine, Johnson City, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study describes a systematic approach to integrate generative artificial intelligence (AI) into our new medical education curriculum at the Quillen College of Medicine while maintaining academic integrity. As medical schools navigate the widespread adoption of large language models, we implemented a five-step process to address the policy recommendations of the Association of American Medical Colleges (AAMC) on AI integration. First, surveys assessed AI usage patterns among students, revealing increasing adoption (from 24% to 77%) between May 2024 and February 2025. Second, clear professionalism guidelines were established, prohibiting AI use in generating learning objectives, writing Subjective, Objective, Assessment, and Plan (SOAP) notes, or completing assignments while permitting its use in research applications or otherwise in specific course settings when given permission to do so by faculty. Third, an institutional grant ensured equitable access to AI platforms for all students entering in Fall 2024. Fourth, AI was integrated into controlled educational settings, particularly within problem-based learning (PBL) for first-year students and team-based learning (TBL) for second-year students, with structured evaluation criteria. Finally, specialized training on ethical AI use was provided to students transitioning to clinical clerkships. Survey data indicated that students found AI exposure beneficial (91% agreement) and helpful for researching learning objectives (94% agreement), though confidence in AI's accuracy was lower (85% agreement). Students prioritized summarizing learning materials and testing understanding as important AI applications while valuing the ability to function as clinicians both with and without AI. Our approach demonstrates a balanced integration strategy that encourages responsible AI adoption while maintaining educational integrity in medical training.

Indexed as

curriculumgenerative artificial intelligenceintegrationmedical educationproblem-based learning (pbl)

Identifiers

PMID40718215
PMCPMC12296314

What OpenQuestion holds

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LicenceCC BY
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 OpenQuestion graph.