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Cited by 42Artificial Intelligence in Healthcare and EducationRadiology practices and educationArtificial Intelligence Applications

An extensive survey of radiographers from the Middle East and India on artificial intelligence integration in radiology practice

Open access
Abuzaid, Mohamed M., Elshami, Wiam, McConnell, Jonathan, Tekin, H. O.·2021·Health and Technology
DOI 10.1007/s12553-021-00583-1PMC PMC8342654
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Abstract

Assessing the current Artificial intelligence (AI) situation is a crucial step towards its implementation into radiology practice. The study aimed to assess radiographer willingness to accept AI in radiology work practice and the impact of AI in work performance. An exploratory cross-sectional online survey conducted for radiographers working within the Middle East and India was conducted from May–August 2020. A previously validated survey used to obtain radiographer's demographics, knowledge, perceptions, organization readiness, and challenges of integrating AI into radiology. The survey was accessible for radiographers and distributed through the societies page. The survey was completed by 549 radiographers distributed as (77.6%, n = 426) from the Middle East while (22.4%, n = 123) from India. A majority (86%, n = 773) agreed that AI currently plays an important role in radiology and (88.0%, n = 483) expected that AI would play a role in radiology practice and image production. The challenges for AI implementation in practice were developing AI skills (42.8%, n = 235) and AI knowledge development (37.0%, n = 203). Participants showed high interest to integrate AI in under and postgraduate curriculum. There is excitement about what AI could offer, but education input is a requirement. Fears are expressed about job security and how radiology may work across all ages and educational backgrounds. Radiographers become aware of AI role and challenges, which can be improved by education and training.

AI snapshot

Written from the paper’s full text.

TL;DR

A cross-sectional survey of 549 radiographers in the Middle East and India found strong support for AI in radiology but limited training and organizational readiness. Education and skills development were the main perceived challenges, with fears about job security expressed across ages and qualifications.

Key findings

  • Most respondents viewed AI positively: 86% (n = 773 as reported) agreed/strongly agreed AI currently plays an important role in radiology, and 88.0% (n = 483) agreed/strongly agreed AI would be encountered in many radiology practice and image-production applications.
  • The biggest implementation challenges were developing AI skills (42.8%, n = 235) and AI knowledge development (37.0%, n = 203). The highest recommended workplace AI applications were dose management (50.3%, n = 276), quality control (49.9%, n = 274), and image evaluation (39.9%, n = 219).
  • Education demand was high: 40% (n = 217) strongly agreed AI should be taught in postgraduate programs, and 30.6% (n = 168) strongly agreed it should be taught in undergraduate programs. 51.0% were self-taught, and 22.4% had attended AI courses.
  • Age differences appeared in threat perception: 37.2% (n = 67) of those under 30 agreed/strongly agreed AI would threaten/disrupt current radiology practice, compared with 53.1% (n = 26) of those over 50; one-way ANOVA showed a significant difference by age group (p = 0.032). 33.7% had no idea whether their organization had an AI strategy, and 38.6% were not sure if someone was responsible for AI.

Methods

  • Exploratory cross-sectional online survey conducted May–August 2020 among diagnostic radiographers working in the Middle East and India (MENAIN), using convenience sampling distributed through professional body society Facebook pages.
  • 549 responses were analyzed: 77.6% (n = 426) from the Middle East and 22.4% (n = 123) from India. The survey adapted a previously validated instrument and covered demographics, knowledge, perceptions, readiness, and challenges of integrating AI into radiology.
  • Data were processed in SPSS version 24; quantitative variables were expressed as percentages, means, and standard deviations, with one-way ANOVA used to assess associations by age and qualification. Graphs were created in Microsoft Excel 2013.

Limitations

  • The paper states limitations including a restricted sample, survey length, and awareness about the study’s importance; it notes that voluntary-response cross-sectional surveys may bias the sample toward those interested in AI.
  • The paper also states that cross-sectional surveys are quick, easy, allow multiple outcomes, and are good for descriptive analyses, but carry uncertainty from voluntary response.
  • Evident from the text: convenience sampling and social-media distribution limit generalizability; the population size was unidentified, so no response rate is reported; and some reported counts are internally inconsistent (e.g., 86% reported as n = 773 when total N = 549), suggesting possible data or reporting errors.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1007/s12553-021-00583-1

Cites

22 of 22 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 2 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    30.6% (n=168) of participants strongly agreed that AI should be taught in the undergraduate program.

    “and 30.6% (n = 168) strongly agreed that AI should be taught in the undergraduate program”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The adapted survey instrument remains valid for measuring radiographers' knowledge, perceptions, readiness, and challenges in the Middle East and India.
    • Rests on an assumption: Participants' self-reported responses accurately reflect their actual knowledge, perceptions, and organizational readiness.
  • Findingown data

    40% (n=217) of participants strongly agreed that AI should be taught in postgraduate programs.

    “Forty percent (n = 217) strongly agreed that AI should be taught in postgraduate programs”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The adapted survey instrument remains valid for measuring radiographers' knowledge, perceptions, readiness, and challenges in the Middle East and India.
    • Rests on an assumption: Participants' self-reported responses accurately reflect their actual knowledge, perceptions, and organizational readiness.
  • Findingown data

    86% of participants agreed or strongly agreed that AI currently plays an important role in radiology.

    “A majority (86%, n = 773) agreed that AI currently plays an important role in radiology”
    • Also stated in: § Results · plain
      “Eighty-six percent (n = 773) of participants agreed and strongly agreed that AI currently plays an important role in radiology”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The adapted survey instrument remains valid for measuring radiographers' knowledge, perceptions, readiness, and challenges in the Middle East and India.
    • Rests on an assumption: Participants' self-reported responses accurately reflect their actual knowledge, perceptions, and organizational readiness.
  • Findingown data

    88.0% of participants agreed or strongly agreed that AI would be encountered in many applications for radiology practice and image production.

    “and (88.0%, n = 483) expected that AI would play a role in radiology practice and image production.”
    • Also stated in: § Results · plain
      “88.0% (n = 483) agreed and strongly agreed that AI would be encountered in many applications for radiology practice and image production”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The adapted survey instrument remains valid for measuring radiographers' knowledge, perceptions, readiness, and challenges in the Middle East and India.
    • Rests on an assumption: Participants' self-reported responses accurately reflect their actual knowledge, perceptions, and organizational readiness.
  • Findingown data

    The post-hoc test did not show any difference among age groups for working knowledge of AI.

    “Still, the post-hoc test did not show any difference among age groups.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The adapted survey instrument remains valid for measuring radiographers' knowledge, perceptions, readiness, and challenges in the Middle East and India.
    • Rests on an assumption: Participants' self-reported responses accurately reflect their actual knowledge, perceptions, and organizational readiness.
  • May combine two claims. 13
  • Classified as probably not a substantial claim. 5

  • May combine two claims.

    Findingown data

    The survey was completed by 549 radiographers, with 77.6% (n=426) from the Middle East and 22.4% (n=123) from India.

    “The survey was completed by 549 radiographers distributed as (77.6%, n = 426) from the Middle East while (22.4%, n = 123) from India.”
    • Also stated in: § Results · plain
      “The result of the study reveals that (77.6%, n = 426) of the respondents worked in the Middle East while (22.4%, n = 123) in India.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Participants' self-reported responses accurately reflect their actual knowledge, perceptions, and organizational readiness.

  • May combine two claims.

    Findingown data

    37.2% (n=67) of participants aged less than 30 years agreed or strongly agreed that AI would threaten/disrupt current radiology practice, compared with 53.1% (n=26) of participants aged over 50 years.

    “While 37.2% (n = 67) of participants aged less than 30 years agreed and strongly agreed that AI would threaten/disrupt current radiology practice(s), in comparison to 53.1% (n = 26) of the participants aged over 50 years identified this would happen.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The adapted survey instrument remains valid for measuring radiographers' knowledge, perceptions, readiness, and challenges in the Middle East and India.
    • Rests on an assumption: Participants' self-reported responses accurately reflect their actual knowledge, perceptions, and organizational readiness.

  • May combine two claims.

    Findingown data

    The biggest challenges for AI implementation in the workplace were development of skills (42.8%, n=235) and knowledge about AI (37.0%, n=203).

    “The challenges for AI implementation in practice were developing AI skills (42.8%, n = 235) and AI knowledge development (37.0%, n = 203).”
    • Also stated in: § Results · plain
      “The biggest challenges for AI implementation in the workplace was the development of skills (42.8%, n = 235) and knowledge about AI (37.0%, n = 203)”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The adapted survey instrument remains valid for measuring radiographers' knowledge, perceptions, readiness, and challenges in the Middle East and India.
    • Rests on an assumption: Participants' self-reported responses accurately reflect their actual knowledge, perceptions, and organizational readiness.

  • May combine two claims.

    Findingown data

    The Post hoc multiple comparisons showed a significant difference between groups of participants who aged >50 years (p=0.04) and hold diploma degree (p=0.031).

    “The result of Post hoc multiple comparisons showed a significant difference between groups of participants who aged > 50 years (p = 0.04) and hold diploma degree (p = 0.031).”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The adapted survey instrument remains valid for measuring radiographers' knowledge, perceptions, readiness, and challenges in the Middle East and India.
    • Rests on an assumption: Participants' self-reported responses accurately reflect their actual knowledge, perceptions, and organizational readiness.

  • May combine two claims.

    Findingown data

    The highest AI application identified as necessary for the workplace was dose management (50.3%, n=276), followed by quality control (49.9%, n=274) and image evaluation (39.9%, n=219).

    “The highest AI application identified as necessary for the workplace was dose management (50.3%, n = 276), followed by quality control (49.9%, n = 274) and image evaluation (39.9%, n = 219) (Fig. 2).”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The adapted survey instrument remains valid for measuring radiographers' knowledge, perceptions, readiness, and challenges in the Middle East and India.
    • Rests on an assumption: Participants' self-reported responses accurately reflect their actual knowledge, perceptions, and organizational readiness.

  • May combine two claims.

    Findingown data

    A one-way ANOVA revealed a significant relationship between age groups, qualifications, and the difficulty of training current employees, with p-values of .031 and .027 for age and education.

    “A one-way ANOVA revealed a significant relationship between age groups, qualifications, and the difficulty of training current employees. With p-values of.031 and.027 for age and education”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The adapted survey instrument remains valid for measuring radiographers' knowledge, perceptions, readiness, and challenges in the Middle East and India.
    • Rests on an assumption: Participants' self-reported responses accurately reflect their actual knowledge, perceptions, and organizational readiness.

  • May combine two claims.

    Interpretationnone given

    Fears are expressed about job security and how radiology may work across all ages and educational backgrounds.

    “Fears are expressed about job security and how radiology may work across all ages and educational backgrounds.”
    • Also stated in: § Conclusion · plain
      “Fears are expressed about job security and how radiology may work, however this is across all ages and educational backgrounds.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The convenience sample of 549 radiographers is representative of radiographers in the Middle East and India, so that the survey results can be generalized to that population.
    • Rests on an assumption: The adapted survey instrument remains valid for measuring radiographers' knowledge, perceptions, readiness, and challenges in the Middle East and India.
    • Rests on an assumption: Participants' self-reported responses accurately reflect their actual knowledge, perceptions, and organizational readiness.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductcitation

    The study used a previously validated research instrument adapted by the research team.

    “For this study, a previously validated research instrument was adapted by the research team [5].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [5] Abuzaid MM, Elshami W, Tekin H, Issa B. Assessment of the Willingness of Radiologists and Radiographers to Accept the Integration of Artificial Intelligence Into Radiology Practice. Acad Radiol. 2020 Oct.

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    The radiographers are aware of the challenges of AI implementation and indicated the knowledge gap as the main concern.

    “The radiographers' aware of the challenges of AI implementation and indicated the knowledge gap as the main concern.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The convenience sample of 549 radiographers is representative of radiographers in the Middle East and India, so that the survey results can be generalized to that population.
    • Rests on an assumption: The adapted survey instrument remains valid for measuring radiographers' knowledge, perceptions, readiness, and challenges in the Middle East and India.
    • Rests on an assumption: Participants' self-reported responses accurately reflect their actual knowledge, perceptions, and organizational readiness.

  • May combine two claims.

    Interpretationown data

    Most respondents see AI as having an important and wide role, with slightly more mixed feelings about the negative or positive impact on careers and service change.

    “Most respondents see AI as having an important and wide role, with slightly more mixed feelings about the negative or positive impact on careers and service change.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The convenience sample of 549 radiographers is representative of radiographers in the Middle East and India, so that the survey results can be generalized to that population.
    • Rests on an assumption: The adapted survey instrument remains valid for measuring radiographers' knowledge, perceptions, readiness, and challenges in the Middle East and India.
    • Rests on an assumption: Participants' self-reported responses accurately reflect their actual knowledge, perceptions, and organizational readiness.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationreasoning

    As a result of the radiographers' limited exposure to AI, there was an even split between excitement and fear of AI.

    “As a result of the radiographers' limited exposure to AI, there was an even split between excitement and fear of AI.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The convenience sample of 549 radiographers is representative of radiographers in the Middle East and India, so that the survey results can be generalized to that population.
    • Rests on an assumption: The adapted survey instrument remains valid for measuring radiographers' knowledge, perceptions, readiness, and challenges in the Middle East and India.
    • Rests on an assumption: Participants' self-reported responses accurately reflect their actual knowledge, perceptions, and organizational readiness.

  • May combine two claims.

    Interpretationreasoning

    Overall, the general message is unprepared for AI or lack of information about this reaching a wider staff base.

    “Overall, however, the general message is unprepared for AI or lack of information about this reaching a wider staff base.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The convenience sample of 549 radiographers is representative of radiographers in the Middle East and India, so that the survey results can be generalized to that population.
    • Rests on an assumption: The adapted survey instrument remains valid for measuring radiographers' knowledge, perceptions, readiness, and challenges in the Middle East and India.
    • Rests on an assumption: Participants' self-reported responses accurately reflect their actual knowledge, perceptions, and organizational readiness.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductnone given

    The study had a restricted sample, survey length and awareness about the study importance as limitations.

    “A part of the study limitation is the restricted sample, survey length and awareness about the study importance.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductnone given

    The data were checked for duplicate responses and filtered to remove responses from areas other than the Middle East and India.

    “The data were checked for any duplicate responses, filtered to remove any responses from areas rather than the Middle East and India.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductreasoning

    Cross-sectional surveys are associated with uncertainty driven on a voluntary response basis, and there may be bias within the sample population representing those with interest in AI.

    “Although cross-sectional surveys are associated with uncertainty being driven on a voluntary response basis, it could be argued that there may be bias within the sample population representing those with interest in AI.”
    Quote in the paperNot a substantial claim
15 of 15

  • The paper relies on this without support.

    The convenience sample of 549 radiographers is representative of radiographers in the Middle East and India, so that the survey results can be generalized to that population.

    “The study used convenience sampling as it is an easy method to access participants in different geographical proximity.”
    “In conclusion, there is strong support for AI integration in radiology work practice among radiographers.”

  • The paper relies on this without support.

    The adapted survey instrument remains valid for measuring radiographers' knowledge, perceptions, readiness, and challenges in the Middle East and India.

    “For this study, a previously validated research instrument was adapted by the research team [5].”

  • The paper relies on this without support.

    Participants' self-reported responses accurately reflect their actual knowledge, perceptions, and organizational readiness.

    “The radiographer's knowledge, perceptions, readiness, and challenges of integrating AI into radiology were captured using multiple response and attitudinal scale questions in parts two and three.”
3 of 3

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

  • Reported agreement count exceeds the sample size

    Major

    The survey had 549 participants. Table 2 records 222 who agreed and 251 who strongly agreed, totaling 473-not 773. The 86% figure is consistent with 473 of 549 participants.

    Reported 773 · recomputed 473

  • Awareness percentage does not match its count

    Minor

    The reported 197 participants are 35.88% of the 549 respondents, not 53.9%.

    Reported 53.9 · recomputed 35.883424

  • Comfort-with-AI percentage does not match its count

    Minor

    The reported 76 participants are 13.84% of the 549 respondents, not 12.2%.

    Reported 12.2 · recomputed 13.843352

3 of 3

Cites

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Cited by

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Related

Authors
Abuzaid, M. M., Elshami, W., McConnell, J., & Tekin, H. O.
Affiliations
  • University of Sharjah (AE)
  • NHS Greater Glasgow and Clyde (GB)
Corresponding author
Abuzaid, Mohamed M.
ORCID
Venue
Health and Technology
Publisher
Springer Science and Business Media LLC
Published
2021-08-06
Volume
11
Issue
5
Pages
1045-1050
ISSN
  • 2190-7188 (ISSN-L)
  • 2190-7196
DOI
10.1007/s12553-021-00583-1
PMC
PMC8342654
OpenAlex
W3191157361
Semantic Scholar
37085ceca2f3bb1f5c9b72f8d41bee452f966a40
Cited by
42
References
22
Citations by year
2026: 2 · 2025: 6 · 2024: 13 · 2023: 6 · 2022: 15
FWCI
2.45
Type
journal article
Language
en
Open access
Licence
Locations
  • Health and Technology (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords
artificial intelligence in radiology, radiographers, artificial intelligence adoption, AI implementation challenges, Middle East, India

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Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-09.

Statistics and arithmetic are recomputed, not judged.

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