Evidence map›Paper›PMID 41393905›Full record

ReviewBJR open2025

Radiology AI training and assessment-challenges, innovations, and a path forward.

Girija Agarwal, Kavish Maroo, Paymon Zomorodian, Naman Bhatt, Dilan Sanli, Akash Sharma, Susan C Shelmerdine

Abstract readReview
In one paragraph

Review in BJR open, 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. Review
  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

7 authors.

Girija AgarwalDepartment of Clinical Radiology, St Mary's Hospital, Imperial College Healthcare Trust, London W2 1NY, United Kingdom.ORCID https://orcid.org/0000-0001-8909-6560
Kavish MarooUniversity College London Medical School, London, WC1E 6DE, United Kingdom.
Paymon ZomorodianDepartment of Clinical Radiology, Mersey and West Lancashire Teaching Hospitals, Prescot, L35 5DR, United Kingdom.
Naman BhattDepartment of Clinical Radiology, West Hertfordshire Teaching Hospital Trust, Hertfordshire, WD18 0HB, United Kingdom.
Dilan SanliDepartment of Clinical Radiology, University Hospital Southampton NHS Foundation Trust, Southampton, SO16 6YD, United Kingdom.
Akash SharmaDepartment of Clinical Radiology, St Mary's Hospital, Imperial College Healthcare Trust, London W2 1NY, United Kingdom.
Susan C ShelmerdineDepartment of Clinical Radiology, Great Ormond Street Hospital, London, WC1N 3BH, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is transforming radiology, with nearly 80% of approved AI as medical devices (AIaMDs) being imaging-related. As AI adoption accelerates, radiology training programs must evolve to equip future radiologists with the skills to critically evaluate, implement, and integrate AI into clinical practice. However, despite AI's growing role, its inclusion in medical curricula remains inconsistent, and assessment of AI competency is lacking. This review explores the current state of AI in UK medical training curricula with a more in-depth focus on radiology. We discuss the potential impact of AI on competency evaluations, including the Fellowship of the Royal College of Radiologists (FRCR) examinations, Annual Review of Competence Progression (ARCP), and on-call assessments. Additionally, we examine how AI-driven educational resources, such as AI-assisted training platforms, could enhance radiology education. To future-proof radiology training and careers, we propose strategies to evaluate AI literacy including nationalized structured AI teaching, and AI-focused assessments. Addressing these challenges will be crucial in ensuring that radiologists remain at the forefront of digital healthcare transformation while maintaining their core diagnostic expertise.

Indexed as

artificial intelligenceassessmenteducationethicsradiology

Identifiers

PMID41393905
PMCPMC12696643

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.