Evidence map›Paper›PMID 39507889›Full record

ReviewPolish journal of radiology2024

Advancing radiology education for medical students: leveraging digital tools and resources.

Thomas Stirrat, Robert Martin, Muhammad Umair, Joseph Waller

Abstract readReview
In one paragraph

Review in Polish journal of radiology, 2024. 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. Article
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.

Thomas StirratGeorgetown University School of Medicine, Georgetown, United States.
Robert MartinInspira Medical Center, Mullica Hill, United States.
Muhammad UmairDepartment of Radiology, Johns Hopkins University, Baltimore, United States.
Joseph WallerChristiana Care, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study evaluates diverse educational resources to address the gaps in diagnostic radiology education for medical students, aiming to identify tools that enhance theoretical knowledge and practical diagnostic skills. Employing a multi-faceted review, we analyzed digital platforms, academic databases, and social media for resources beneficial to medical students in radiology, assessing their accessibility, content quality, and educational value. Our investigation uncovered a broad spectrum of resources, from foundational platforms to advanced simulation tools, varying in their approach to teaching radiology. Traditional resources provide essential theoretical knowledge, while digital tools, including interactive case studies and multimedia content, offer immersive learning experiences. Notably, resources integrating machine learning and social media facilitate dynamic, peer-to-peer learning and up-to-date case discussions. Despite the minimal current focus on VR, its role in enhancing interactive learning is notable. The diversity in educational tools highlights the evolving nature of radiology education, reflecting a shift towards more engaging and practical learning methodologies. Identifying and integrating a variety of educational resources into radiology education can significantly enhance learning outcomes for medical students, preparing them for the complexities of modern diagnostic radiology with a well-rounded educational approach.

Indexed as

case-based learning in radiologydigital learning toolsinteractive radiology resourcesmedical student radiology trainingonline radiology educationradiology education

Identifiers

PMID39507889
PMCPMC11538907

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.