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

Laura Mc Laughlin; Sonyia McFadden; Angelina Villikudathil; Jonathan McConnell; Ciara Hughes; RR Bond; Clare Rainey

doi:10.1002/jmrs.850

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

Laura Mc Laughlin
, Sonyia McFadden
, Angelina Villikudathil
, Jonathan McConnell
, Ciara Hughes
, RR Bond
, Clare Rainey

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

67 Downloads (Pure)

Abstract

Introduction
Artificial 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.

Methods
Radiographers 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.

Results
Fourteen 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.

Conclusion
The 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.

Original language	English
Pages (from-to)	209-216
Number of pages	8
Journal	Journal of Medical Radiation Sciences
Volume	72
Issue number	2
Early online date	20 Dec 2024
DOIs	https://doi.org/10.1002/jmrs.850
Publication status	Published (in print/issue) - 30 Jun 2025

Bibliographical note

© 2024 The Author(s). Journal of Medical Radiation Sciences published by John Wiley & Sons Australia, Ltd on behalf of Australian Society of Medical Imaging and Radiation Therapy and New Zealand Institute of Medical Radiation Technology.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Funding

The authors would like to acknowledge the support of the College of Radiographers Research Industry Partnership Award Scheme (CoRIPS) for funding this study.

Funders	Funder number
???publication-publication-funding-organisation-not-added???	College of Radiographers Research Industry Partnership Award Scheme (CoRIPS)
CoRIPS

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 8 Decent Work and Economic Growth
SDG 9 Industry, Innovation, and Infrastructure

Keywords

checklist
artiﬁcial intelligence
image
interpretation
Musculoskeletal
musculoskeletal
artificial intelligence
image interpretation

Access to Document

10.1002/jmrs.850Licence: CC BY

J of Medical Radiation Sci - 2024 - McLaughlin - An evaluation of a checklist in musculoskeletal radiographic imageFinal published version, 482 KBLicence: CC BY

Cite this

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title = "An evaluation of a checklist in musculoskeletal radiographic image interpretation when using artificial intelligence",

abstract = "Introduction Artificial 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. Methods Radiographers 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. Results Fourteen 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. Conclusion The 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.",

keywords = "checklist, artiﬁcial intelligence, image, interpretation, Musculoskeletal, musculoskeletal, artificial intelligence, image interpretation",

author = "\{Mc Laughlin\}, Laura and Sonyia McFadden and Angelina Villikudathil and Jonathan McConnell and Ciara Hughes and RR Bond and Clare Rainey",

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AU - McFadden, Sonyia

AU - Villikudathil, Angelina

AU - McConnell, Jonathan

AU - Hughes, Ciara

AU - Bond, RR

AU - Rainey, Clare

N1 - © 2024 The Author(s). Journal of Medical Radiation Sciences published by John Wiley & Sons Australia, Ltd on behalf of Australian Society of Medical Imaging and Radiation Therapy and New Zealand Institute of Medical Radiation Technology.

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N2 - Introduction Artificial 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. Methods Radiographers 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. Results Fourteen 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. Conclusion The 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.

AB - Introduction Artificial 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. Methods Radiographers 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. Results Fourteen 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. Conclusion The 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.

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