Evidence map›Paper›PMID 39707714›Full record

ArticleJournal of medical radiation sciences2025

An evaluation of a checklist in musculoskeletal radiographic image interpretation when using artificial intelligence.

Laura McLaughlin, Sonyia L McFadden, Angelina T Villikudathil, Jonathan McConnell, Ciara Hughes, Raymond Bond, Clare Rainey

Abstract readEvaluation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of medical radiation sciences, 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

7 authors.

Laura McLaughlinSchool of Health Sciences, Faculty of Life and Health Sciences, Ulster University, Belfast, Northern Ireland, UK.ORCID https://orcid.org/0000-0001-7825-6821
Sonyia L McFaddenSchool of Health Sciences, Faculty of Life and Health Sciences, Ulster University, Belfast, Northern Ireland, UK.
Angelina T VillikudathilSchool of Health Sciences, Faculty of Life and Health Sciences, Ulster University, Belfast, Northern Ireland, UK.ORCID https://orcid.org/0009-0003-6057-5359
Jonathan McConnellSchool of Health Sciences, Faculty of Life and Health Sciences, Ulster University, Belfast, Northern Ireland, UK.
Ciara HughesSchool of Health Sciences, Faculty of Life and Health Sciences, Ulster University, Belfast, Northern Ireland, UK.
Raymond BondSchool of Computing, Faculty of Computing, Engineering and the Built Environment, Ulster University, Belfast, Northern Ireland, UK.
Clare RaineySchool of Health Sciences, Faculty of Life and Health Sciences, Ulster University, Belfast, Northern Ireland, UK.ORCID https://orcid.org/0000-0003-0449-8646

Funding

College of Radiographers Research Industry Partnership Award Scheme (CoRIPS)
6 · The paper itself

Abstract

introductionArtificial intelligence (AI) is being used increasingly in image interpretation tasks. Human reliance on technology and bias can cause decision errors. A checklist, used with the AI to mitigate against such biases, may optimise the use of AI technologies and promote good decision hygiene. A checklist to aid radiographic image interpretation for radiographers using AI for image interpretation was formed. This study investigates the effect of a checklist for musculoskeletal (MSK) radiographic image assessment when using AI interpretive assistance.

methodsRadiographers were asked to interpret five MSK examinations with AI feedback. They were then provided with the checklist and asked to reinterpret the same five examinations with the AI feedback (n = 140 interpretations). During the interpretation sessions, participants were asked to provide a diagnosis and a confidence level on the diagnosis provided. Participants were then asked to complete a questionnaire to gain feedback on the use of the checklist.

resultsFourteen radiographers were recruited. Nine participants found the checklist alongside the AI most useful and five participants found the AI element to be most useful on its own. Five participants found the AI feedback to be useful as it helped to critique the radiographic image interpretation more closely and rethink their own initial diagnosis.

conclusionThe checklist for use with AI in MSK image interpretation contained useful elements to the user, but further developments can be made to enhance its use in clinical practice.

Indexed as

Artificial IntelligenceChecklistMusculoskeletal DiseasesMusculoskeletal SystemRadiographic Image Interpretation, Computer-AssistedHumansartificial intelligencechecklistimage interpretationmusculoskeletal

Identifiers

PMID39707714
PMCPMC13014785

What OpenQuestion holds

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Registered trials

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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.