Evidence mapPaperPMID 41756231Full record

ReviewSudanese journal of paediatrics2025

The augmentative role of artificial intelligence in medical education and healthcare practices: an integrative review.

Abdelaziz Elamin, Afaq Mohamed

Abstract readReview
In one paragraph

Review in Sudanese journal of paediatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Abdelaziz ElaminCollege of Medicine and Health Sciences, Arabian Gulf University, Manama, Bahrain.
Afaq MohamedGlasgow Royal Infirmary, Glasgow, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Medical education is rapidly evolving from time-based, teacher-centered models toward competency-focused, learner-centered approaches that integrate technology, clinical simulation, workplace learning, and attention to learners' wellbeing and social accountability. Artificial intelligence (AI), broadly defined as computational methods that perform tasks that would normally require human intelligence, comprehensively and in a very short time. The landscape of medical education is undergoing a significant transformation, driven by the rapid integration of advanced clinical simulation and AI into medical curricula. This narrative review summarizes current global applications, opportunities, and challenges of AI in training the next generation of healthcare professionals. We examine how AI is evolving from a new tool to a core part of the educational system, enabling personalized learning through adaptive platforms, improving clinical reasoning via simulated environments, and offering objective metrics for assessment and feedback. Specific applications such as AI-powered virtual patients, natural language processing for communication skills training, and the analysis of surgical simulation data are discussed. Additionally, the review addresses important ethical issues, including data privacy, algorithmic bias, and the potential for dehumanizing medicine. It emphasizes the urgent need for international collaboration on curriculum development to ensure fair and effective integration of AI in medical education and health practices. While many challenges remain, the potential of AI to create a more efficient, standardized, and student-focused model of medical education worldwide is tremendous, promising to develop a new generation of physicians ready to handle the complexities of modern healthcare.

Indexed as

Computer-based learningEthicsGenerative AITeaching strategies

Identifiers

PMID41756231
PMCPMC12934489

What Socratic holds

Textmetadata
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.