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. 2025 Mar 27;8(4):e70465.
doi: 10.1002/hsr2.70465. eCollection 2025 Apr.

Exploring Radiographers' Readiness for Artificial Intelligence in Kuwait: Insights and Applications

Asseel Khalaf  1 , Manar Alshammari  2 , Hawraa Zayed  3 , Maryam Emnawer  4 , Abdulmohsen Esfahani  3
Affiliations

Exploring Radiographers' Readiness for Artificial Intelligence in Kuwait: Insights and Applications

Asseel Khalaf et al. Health Sci Rep. .

Abstract

Introduction: There is a growing adoption of artificial intelligence (AI) in the field of medical imaging. AI can potentially enhance patient care, improve workflow, and analyze patient's medical data. This study aimed to explore radiographers' knowledge, perceptions, and expectations toward integrating AI into medical imaging and to highlight one of the available applications of AI by evaluating an AI-based software that generates chest reports.

Methods: A cross-sectional survey was distributed to radiographers (n = 50) requesting information regarding demographics and knowledge of AI. In the retrospective part, chest radiographs were collected (n = 40), and an AI report was generated using Siemens AI software. A Likert scale was used by a radiologist to rate the report's accuracy. Ethical approval was obtained. Data are presented as mean ± SD.

Results: The survey results showed that most participants agreed that radiographers must adapt the AI technology, and they showed interest in taking courses about AI within radiography (98%, 92%, n = 50). Participants' opinions on AI correlated with their perceptions of AI education (p < 0.05, r = 0.307). The findings from the retrospective study showed that the radiologist agreed with 53% of the AI-generated chest reports.

Conclusion: The study findings identified a need for AI education and training for radiographers to increase their knowledge and improve their ability to use AI. Additionally, the study demonstrated that AI-powered tools are showing great promise in the field of medical imaging.

Keywords: AI applications; artificial Intelligence; perceptions; radiography.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Percentage distribution of participants by demographic characteristics and work experience. (A) Percentage distribution of participants by gender; (B) percentage distribution by age; and (C) percentage distribution of participants by work experiences.
Figure 2
Figure 2
Percentage distribution of baseline understanding of the application of AI in medical imaging.
Figure 3
Figure 3
Likert scale bar chart summary of the participants' familiarity with AI (n = 50).
Figure 4
Figure 4
Likert scale bar chart summary of the participants' opinions on AI (n = 50).
Figure 5
Figure 5
Likert scale bar chart summary of the participants' perceptions of AI education within radiography (n = 50).
Figure 6
Figure 6
Likert scale bar chart summary of the participants' perceptions of the effects of AI on future roles (n = 50).
Figure 7
Figure 7
The Likert scale rating summary for the radiologist ranging from agree to disagree (n = 40).
Figure 8
Figure 8
Example of an AI‐generated chest X‐ray report with markings indicating the pathological findings with the AI confidence scores.
See this image and copyright information in PMC

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