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Peer-reviewedCC BYFrontiers in medicine

Attitudes and perceptions toward artificial intelligence among practicing rheumatologists in Iraq: a cross-sectional web-based survey

  • Mustafa Alhayali1
  • Marwa Moayad1
  • Nizar Abdulatif Jasim2
  • Zaid Al-Zuhairi2
  • Marwah Al-Mashhadani3
  • Mustafa A. Shahrori4
  • Rami Al-Khaffaf5
  • Marafi Jammaa Ahmed6
  • Enas Osama Hassan Omer6
  • Aya Ahmed Shimal2
  • Rafal Othman7
  • Ahmed Dheyaa Al-Obaidi2
7 affiliations
  1. 1Department of Rheumatology, College of Medicine, Ibn Sina University of Medical and Pharmaceutical Sciences , ,
  2. 2College of Medicine, University of Baghdad , ,
  3. 3Department of Internal Medicine, Aldujail Hospital , ,
  4. 4Faculty of Medicine, An-Najah National University , ,
  5. 5Department of Internal Medicine, Henry Ford St. John Hospital , , ,
  6. 6Faculty of Medicine, University of Bahri , ,
  7. 7Albany Medical College , , ,
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Abstract

Background: Artificial intelligence is becoming a bigger part of healthcare, particularly in rheumatology, where clinicians must integrate complex clinical, laboratory, and imaging data. Physicians’ perceptions of AI may substantially influence the extent to which it is implemented responsibly in routine clinical practice.

Objective: This study aimed to assess the attitudes of practicing rheumatologists in Iraq toward artificial intelligence and to examine their association with demographic and professional characteristics.

Methods: A four-month web-based cross-sectional survey was conducted from October 2025 to January 2026. A questionnaire adapted from previous studies was refined through expert review and pilot testing before distribution. The questionnaire comprised demographic information, attitude statements measured on a five-point Likert scale, and items assessing general perceptions of artificial intelligence. Composite attitude scores were categorized using modified Bloom's cutoff criteria. Statistical analysis was performed using SPSS and R.

Results: A total of 162 complete responses were collected. Most respondents were aged 35–44 years (42.0%), male (56.8%), and specialists (67.9%). Overall, 61.7% reported familiarity with AI and 69.1% had already used AI in rheumatology practice. Most participants 56.8% exhibited a poor attitude, 32.1% a moderate attitude, and 11.1% a good attitude toward AI. Bivariate analysis identified familiarity with AI (p=0.003) and involvement in research (p=0.009) as significantly associated with more favorable attitudes.

Conclusion: Iraqi rheumatologists demonstrate cautious engagement with artificial intelligence, with the majority displaying poor-to-moderate overall attitude scores on the standardized composite scale. Greater familiarity with AI and research involvement were associated with more favorable attitudes, highlighting the importance of targeted digital literacy and specialty-specific education to support effective AI adoption in clinical practice.

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Cite

BibTeXRIS
BibTeX
@article{alhayali2026attitudes,
  title = {{Attitudes and perceptions toward artificial intelligence among practicing rheumatologists in Iraq: a cross-sectional web-based survey}},
  author = {Mustafa Alhayali and Marwa Moayad and Nizar Abdulatif Jasim and Zaid Al-Zuhairi and Marwah Al-Mashhadani and Mustafa A. Shahrori and Rami Al-Khaffaf and Marafi Jammaa Ahmed and Enas Osama Hassan Omer and Aya Ahmed Shimal and Rafal Othman and Ahmed Dheyaa Al-Obaidi},
  journal = {Frontiers in medicine},
  year = {2026},
  month = oct,
  doi = {10.3389/fmed.2026.1896672},
  url = {https://europepmc.org/article/PMC/PMC13719341},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Attitudes and perceptions toward artificial intelligence among practicing rheumatologists in Iraq: a cross-sectional web-based survey
AU  - Mustafa Alhayali
AU  - Marwa Moayad
AU  - Nizar Abdulatif Jasim
AU  - Zaid Al-Zuhairi
AU  - Marwah Al-Mashhadani
AU  - Mustafa A. Shahrori
AU  - Rami Al-Khaffaf
AU  - Marafi Jammaa Ahmed
AU  - Enas Osama Hassan Omer
AU  - Aya Ahmed Shimal
AU  - Rafal Othman
AU  - Ahmed Dheyaa Al-Obaidi
PY  - 2026
DA  - 2026/10/16
JO  - Frontiers in medicine
DO  - 10.3389/fmed.2026.1896672
UR  - https://europepmc.org/article/PMC/PMC13719341
AB  - Background: Artificial intelligence is becoming a bigger part of healthcare, particularly in rheumatology, where clinicians must integrate complex clinical, laboratory, and imaging data. Physicians’ perceptions of AI may substantially influence the extent to which it is implemented responsibly in routine clinical practice.  Objective: This study aimed to assess the attitudes of practicing rheumatologists in Iraq toward artificial intelligence and to examine their association with demographic and professional characteristics.  Methods: A four-month web-based cross-sectional survey was conducted from October 2025 to January 2026. A questionnaire adapted from previous studies was refined through expert review and pilot testing before distribution. The questionnaire comprised demographic information, attitude statements measured on a five-point Likert scale, and items assessing general perceptions of artificial intelligence. Composite attitude scores were categorized using modified Bloom's cutoff criteria. Statistical analysis was performed using SPSS and R.  Results: A total of 162 complete responses were collected. Most respondents were aged 35–44 years (42.0%), male (56.8%), and specialists (67.9%). Overall, 61.7% reported familiarity with AI and 69.1% had already used AI in rheumatology practice. Most participants 56.8% exhibited a poor attitude, 32.1% a moderate attitude, and 11.1% a good attitude toward AI. Bivariate analysis identified familiarity with AI (p=0.003) and involvement in research (p=0.009) as significantly associated with more favorable attitudes.  Conclusion: Iraqi rheumatologists demonstrate cautious engagement with artificial intelligence, with the majority displaying poor-to-moderate overall attitude scores on the standardized composite scale. Greater familiarity with AI and research involvement were associated with more favorable attitudes, highlighting the importance of targeted digital literacy and specialty-specific education to support effective AI adoption in clinical practice.
SN  - 2296-858X
ER  -