Evidence map›Paper›PMID 41092421›Full record

ArticleJMIR medical education2025

From Hype to Implementation: Embedding GPT-4o in Medical Education.

Sumaia Sabouni, Mohammad-Adel Moufti, Mohamed Hassan Taha

Abstract read
In one paragraph

Article in JMIR medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Sumaia SabouniDepartment of Data Science and Artificial Intelligence, Faculty of Information Technology, Al-Ahliyya Amman University, Amman, Jordan.ORCID 0009-0001-1393-6794
Mohammad-Adel MouftiCollege of Dental Medicine, University of Sharjah, Sharjah, 27272, United Arab Emirates, 971 65057314.ORCID 0000-0002-7043-8249
Mohamed Hassan TahaMedical Education Center, University of Sharjah, Sharjah, United Arab Emirates.ORCID 0000-0003-0808-5590

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unlabelled: The release of GPT-4 Omni (GPT-4o), an advanced multimodal generative artificial intelligence (AI) model, generated substantial enthusiasm in the field of higher education. However, one year later, medical education continues to face significant challenges, demonstrating the need to move from initial experimentation with the integration of multimodal AIs in medical education toward meaningful integration. In this Viewpoint, we argue that GPT-4o's true value lies not in novelty, but in its potential to enhance training in communication skills, clinical reasoning, and procedural skills by offering real-time simulations and adaptive learning experiences using text, audio, and visual inputs in a safe, immersive, and cost-effective environment. We explore how this innovation has made it possible to address key medical educational challenges by simulating realistic patient interactions, offering personalized feedback, and reducing educator workloads and costs, where traditional teaching methods struggle to replicate the complexity and dynamism of real-world clinical scenarios. However, we also address the critical challenges of this approach, including data accuracy, bias, and ethical decision-making. Rather than seeing GPT-4o as a replacement, we propose its use as a strategic supplement, scaffolded into curriculum frameworks and evaluated through ongoing research. As the focus shifts from AI novelty to sustainable implementation, we call on educators, policymakers, and curriculum designers to establish governance mechanisms, pilot evaluation strategies, and develop faculty training. The future of AI in medical education depends not on the next breakthrough, but on how we integrate today's tools with intention and rigor.

Indexed as

Artificial IntelligenceEducation, MedicalClinical CompetenceHumansartificial intelligenceGPT-4ointeractive learningmedical educationmultimodal generative pretraining transformers

Identifiers

PMID41092421
PMCPMC12527310

What OpenQuestion holds

Textmetadata
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.