Abstract
Objectives
The objective of this study was to evaluate radiologists’ and radiographers’ opinions and perspectives on artificial intelligence (AI) and its integration into the radiology department. Additionally, we investigated the most common challenges and barriers that radiologists and radiographers face when learning about AI.
Methods
A nationwide, online descriptive cross-sectional survey was distributed to radiologists and radiographers working in hospitals and medical centres from May 29, 2023 to July 30, 2023. The questionnaire examined the participants’ opinions, feelings, and predictions regarding AI and its applications in the radiology department. Descriptive statistics were used to report the participants’ demographics and responses. Five-points Likert-scale data were reported using divergent stacked bar graphs to highlight any central tendencies.
Results
Responses were collected from 258 participants, revealing a positive attitude towards implementing AI. Both radiologists and radiographers predicted breast imaging would be the subspecialty most impacted by the AI revolution. MRI, mammography, and CT were identified as the primary modalities with significant importance in the field of AI application. The major barrier encountered by radiologists and radiographers when learning about AI was the lack of mentorship, guidance, and support from experts.
Conclusion
Participants demonstrated a positive attitude towards learning about AI and implementing it in the radiology practice. However, radiologists and radiographers encounter several barriers when learning about AI, such as the absence of experienced professionals support and direction.
Advances in knowledge
Radiologists and radiographers reported several barriers to AI learning, with the most significant being the lack of mentorship and guidance from experts, followed by the lack of funding and investment in new technologies.
Keywords: artificial intelligence, radiologist, radiographer
Introduction
In radiology, the advancements in the medical imaging modalities have led to an exponential growth in the volume of the generated images. This creates a significant challenge for radiologists who must efficiently and accurately examine and diagnose substantial amount of data. Furthermore, urgent and critical cases often due to time constraints, place radiologists under additional pressure since they need to balance speed and accuracy. All of these factors can lead to increased radiologist fatigue, mental exertion, and burnout, potentially affecting their decision-making abilities. To address these pressures and challenges, the field of radiology research has been constantly exploring methods to improve the workflow, enhance diagnostic accuracy, and provide assistance and support to radiology personnel. Recent advances in artificial intelligence (AI) have the potential to assist radiologists in image analysis, reconstruction,1–5 and the detection of abnormalities3,4,6–8 by employing advanced algorithms and machine-learning models.
In addition to radiologists, radiographers play a critical role in the radiology department, ensuring that imaging procedures are carried out accurately and efficiently. Radiographers encounter potential challenges, including selecting the correct exposure factors, addressing positioning issues, optimizing radiation doses, and managing image post-processing errors, all while facing similar time pressures. These errors can potentially be avoided, and performance can be improved through the integration of AI.1–4,9
However, before the AI technology can be applied in the clinical setting, it is of paramount importance to understand and address the opinions, feelings, and concerns of its users. Strategic planning for incorporating AI will enhance the chances of its successful implementation. Therefore, the purpose of this study was to assess the opinions and perspectives of Jordanian radiologists’ and radiographers’ regarding AI and its integration into the medical imaging department.
Methods
After receiving the ethical approval at our institution (Grant number 20230225), an online descriptive cross-sectional survey (see supplementary material) was created using Google forms. Radiologists and radiographers working in hospitals and medical centres in Jordan were invited to participate in this voluntary study. The participants were recruited from public (7 hospitals), military (3 hospitals), private (5 hospitals), and university affiliated hospitals (2 hospitals). The survey package included the survey form, a consent form, a cover letter, and an information sheet explaining the study’s objectives. The survey, adapted from a study conducted by the European Society of Radiology (ESR),10 comprised 3 sections. The first section collected demographic information from the participants including age, type of qualification, years of experience, working sector, and highest academic degree. No personal data were collected. The second and third sections consisted of 20 multiple choice questions. The second section explored the participants’ opinions, feelings, and predictions in regard to AI and its applications in the radiology department. The third section investigated the barriers preventing radiology personnel from learning about AI and its applications.
The survey was piloted on 10 participants, consisting of 5 radiologists and 5 radiographers with varying years of experience, to assess its readability and clarity. Based on their feedback, some questions were reworded for better clarity. The final version of the survey was distributed from May 29, 2023 to July 30, 2023.
Statistical analysis
Data analysis was conducted using IBM (Statistical Package for Social Sciences) SPSS v22. Participants’ demographics were reported using frequencies for categorical data and mean with SD for continuous data. Their responses on the survey questions were represented using frequencies in bar graphs. Five-points Likert-scale data were reported using divergent stacked bar graphs to illustrate central tendencies. To compare between radiologists’ and radiographers’ data concerning their education, opinions, and trust regarding the use of AI and its impact on their work, we employed the chi-square test, with the significance level set at P ≤ .05.
Results
Demographics
The questionnaire was sent to 300 potential participants, from which 263 survey responses were collected. Five incomplete surveys were excluded from the analysis. Two hundred and fifty-eight full surveys were analysed (response rate 86%), consisting of responses from radiologists (n = 101) and radiographers (n = 157). Table 1 demonstrates the demographic information of the participants including age, highest education level, working sector, and years of experience. Among the participants there were 13 (12.9%) junior residents, 27 (26.7%) senior residents, and 61 (60.4%) board certified radiologists. Among the radiographers, 36 (22.9%) had a diploma, 87 (55.4%) had a bachelor’s degree, and 34 (21.7%) had a postgraduate degree. The participants represented all major health sectors in Jordan.
Table 1.
Demographic information of the participants.
| Radiologists number (%) | Radiographers number (%) | ||||
|---|---|---|---|---|---|
| Age, mean (SD) | 38.3 (8.4) | 29.9 (7.5) | |||
| Education level | Junior resident (first & second year) | 13 (12.9%) | Diploma | 36 (22.9%) | |
| Senior resident (third & fourth year) | 27 (26.7%) | Bachelors | 87 (55.4%) | ||
| Board certified radiologist | 61 (60.4%) | Postgrad | 34 (21.7%) | ||
| Working sector | University hospital | 16 (15.8%) | 23 (14.6%) | ||
| Private hospital | 24 (23.8%) | 56 (35.7%) | |||
| Public hospital | 38 (37.6%) | 55 (35.1%) | |||
| Military hospital | 23 (22.8%) | 23 (14.6%) | |||
| Years of experience | 1-5 | 38 (37.6%) | 92 (58.5%) | ||
| 6-10 | 32 (31.7%) | 23 (14.6%) | |||
| 11-15 | 18 (17.9%) | 21 (13.4%) | |||
| 16-20 | 6 (5.9%) | 14 (9%) | |||
| >20 | 7 (6.9%) | 7 (4.5%) | |||
| Total number of participants | 101 (100%) | 157 (100%) | |||
Abbreviations: SD = standard deviation; % = the percent of the participants.
Perception towards implementing AI in the radiology department
The majority of participants (68.3% radiologists and 82.8% radiographers) expressed a favourable attitude towards introducing AI into radiology practice. However, a quarter of the radiologists (25.7%) believed that AI might replace their jobs, compared to 40.8% of the radiographers. This demonstrates a significant difference between the two groups (P = .01). Table 2 illustrates the participants’ perceptions of implementing AI in radiology practice.
Table 2.
Perception regarding the implementation of AI in the radiology practice.
| Questions | Radiologist |
Radiographer |
P-value | ||
|---|---|---|---|---|---|
| No. that answered Yes (%) |
No. that answered No (%) |
No. that answered Yes (%) |
No. that answered No (%) |
||
| Do you trust AI’s ability to analyse data for decision-making? | 60 (59.5) | 41 (40.5) | 124 (79) | 33 (21) | <.01* |
| Did you receive any formal education in any aspect of AI? | 48 (47.5) | 53 (52.5) | 72 (45.9) | 85 (54.1) | .07 |
| Are you interested in learning about AI applications? | 73 (72.3) | 28 (27.7) | 139 (88.5) | 18 (11.5) | <.01* |
| Do you think that AI will replace your job? | 26 (25.7) | 75 (74.3) | 64 (40.8) | 93 (59.2) | .01* |
| Are you in favour of introducing AI in radiology practice? | 69 (68.3) | 32 (31.7) | 130 (82.8) | 27 (17.2) | .01* |
| Do you expect an AI impact on radiologist/radiographer’s life in terms of the number of job positions in the next 5-10 years? | 34 (33.7) | 67 (66.3) | 74 (47.1) | 83 (52.9) | .03* |
Statistical significance.
Abbreviation: AI = artificial intelligence.
Although there was no statistically significant difference among participants who received any type of formal education about AI (radiologists: 47.5%, radiographers: 45.9%, P = .07), a significant difference existed in the level of trust in AI’s ability to analyse data for decision-making between radiologists 59.5% and radiographers 79% (P < .01).
Radiology subspecialties and AI
Radiologists (30.7%) and radiographers (21.7%) both identified breast imaging as the subspecialty most likely to be impacted by the AI revolution. For radiographers, this was followed by neuroradiology (15.9%), while for radiologists, it was nuclear imaging (14.9%). The subspecialty predicted to be least influenced by AI in the next 5-10 years was musculoskeletal imaging by radiographers (1.9%) and interventional imaging by radiologists (2.0%) (see Figure 1).
Figure 1.
Participants perception regarding which radiology subspecialty will be influenced more by artificial intelligence (AI) in the next 5-10 years.
Radiology modalities and AI
Both radiologists (22.8%) and radiographers (26.1%) identified MRI as the modality that would be the most important field for AI applications (assisting in abnormality detection and improving the clinical outcomes), in the next 5-10 years. This was followed by mammography for radiologists (20.8%) and CT for radiographers (18.5%) as illustrated in Figure 2. Notably, none of the radiologists and only 1.9% of the radiographers selected Dual Energy X-ray Absorptiometry DEXA as a significant modality for AI implementations throughout the next decade.
Figure 2.
Participants perception regarding which imaging modality will be the most important field of artificial intelligence (AI) applications in the next 5-10 years. DXA: Dual-energy X-ray Absorptiometry.
AI applications as aids in the radiology department
For radiographers, the most relevant AI applications chosen as aids were imaging protocol optimization (43.3%), lesion characterization (35.0%), and image post-processing (33.8%). Among radiologists, the AI application aids with the highest number of selections were lesion characterization (30.7%), image post-processing (30.7%), and staging/restaging in oncology (28.7%). Figure 3 illustrates the percentage of participants and their choices regarding which AI application they considered more relevant as aids in medical imaging.
Figure 3.
Participants perception regarding which artificial intelligence (AI) applications are more relevant as aids in radiology. The participants were allowed to choose up to 3 options each.
Barriers that prevent radiologists and radiographers from learning about AI
The most significant challenge faced by both radiologists (70%) and radiographers (81.5%) when learning about AI and its applications was the lack of mentorship, guidance, and support from experts in the AI field. Furthermore, radiologists identified a lack of time to learn new technologies as another important barrier, with the same percentage of votes. Approximately half (50.5%) of the radiologists and a similar proportion of radiographers (45.9%) expressed that fear of the unknown was a barrier in embracing AI technology (see Figure 4).
Figure 4.
Barriers that the participants listed as reasons preventing them from learning about artificial intelligence (AI) and its applications. The participants were allowed to choose up to 3 options each.
When exploring participants’ concerns regarding the impact of AI technology on their work, including workload reduction, AI applications in the radiology department, its effects on radiology practice, and the future impact on participants’ income, we observed a trend of disagreement regarding the future impact that AI will have on these factors. This trend of disagreement was more pronounced among radiologists compared to radiographers. Figure 5 illustrates the participants’ opinions regarding the future impact of AI technology on the radiology department.
Figure 5.
Divergent bar graph of Likert-scale data of the radiologists’ and radiographers’ opinions regarding the future impact of artificial intelligence (AI) on their work in the radiology department.
Discussion
Artificial intelligence has the ability to significantly impact radiology practice, revolutionizing medical imaging capabilities. Its ability to enhance diagnostic accuracy, process large volumes of images rapidly, and assist in early disease detection will enable radiologists to diagnose patients efficiently and without delays.11–15 On the other hand, applied mathematics literature demonstrates that AI may carry potential pitfalls and biases that could occur at any stage of the system development; from problem definition (selection bias),16,17 data acquisition and collection (failure to obtain sufficiently large dataset, or limited patient diversity),17,18 model training and development (model under-fitting or overfitting),19,20 and performance evaluation (high costs of system validation).21 Additionally, there is currently limited experience in utilizing AI for patient care across diverse clinical settings.22
However, for the successful integration and implementation of AI in the radiology department, it is crucial to understand the opinions and address the challenges and barriers faced by the radiology staff.
In our study, a notable difference was observed, with significantly more radiographers expressing trust in AI’s data analysis capabilities compared to radiologists. Several potential reasons may explain this finding. Firstly, participants’ opinions may vary, possibly influenced by their individual past experiences. Another reason could be that radiographers might have greater confidence in AI’s technological abilities due to their daily interactions with imaging equipment and technology, allowing them to witness first-hand the benefits of technological advancements.23
In our analysis, we observed a positive attitude towards the implementation of AI; 72.3% of radiologists and 88.5% radiographers expressed interest in learning about AI, and 68.3% of radiologists and 82.8% of radiographers favoured introducing AI in radiology practice. Abuzaid et al24 also reported that two-thirds of the participants in their study (34 radiologists and 119 radiographers) were in favour of implementing AI in radiology. Similar favourable opinions about embracing AI technology in the radiology department were described by two recent large-scale studies conducted by the ESR (n = 675),10 and Huisman et al25 (n = 1041) in predominantly European countries. Regarding the impact of AI on job opportunities, 25.7% of radiologists and 40.8% of the radiographers in our study predicted a reduction. This was in comparison to 42% and 39% of the participants in the ESR study10 and the study by Huisman et al,25 respectively.
In our findings, we observed a strong consensus between radiologists and radiographers regarding breast imaging being the subspecialty most likely to be influenced by AI applications in the next decade. Similar findings were reported by the ESR team10 and Waymel et al,26 where breast imaging was also predicted to be one of the top 3 subspecialties impacted by AI. This is not surprising, given that AI and machine learning have proved effective in breast cancer detection27,28 and are demonstrating performance comparable to experienced radiologists. Furthermore, our participants predicted neuroimaging to be one of the top 3 subspecialties that will be influenced by AI in the future. This may be attributed to the current and ongoing research in applying AI technology in neuroimaging projects, such as mapping brain connectivity, improving our understanding of brain function, and enhancing stroke detection.29,30
Despite participants having diverse educational backgrounds, both groups identified MRI, mammography and CT as the 3 most important modalities for AI application in the field. Comparable findings were reported by the ESR team and Waymel et al26 with their participants also identifying CT, MRI, and mammography as the primary modalities for AI technology implementation.10 This alignment could be attributed to the substantial volume of digital images produced by CT and MRI per patient. Consequently, radiologists may frequently require assistance tools to efficiently analyse these extensive datasets, especially considering the time-consuming nature of this task. Additionally, these 3 modalities are commonly employed in screening, lesion detection, and monitoring, where AI technology has shown promising results.27,28,31 Notably, DEXA scan, was not chosen as a likely modality of AI application by any of the radiologists and only 1.9% of the radiographers in our study. This is likely due to the simplicity of interpreting DEXA results, negating the need for AI assistance. Moreover, AI algorithms often require large image datasets for training, while DEXA scans primarily produce less numerical data.
Regarding AI applications that are more relevant as aids in radiology, it is unsurprising that most of the participants chose lesion characterization (radiologists 30.7%, radiographers 35.0%), and image post processing (radiologists 30.7%, radiographers 33.8%). Their choices align with research on machine-learning algorithms that have proven valuable in several aspects. Firstly, machine-learning algorithms have demonstrated their capability in lesion characterization, including tasks, such as differentiating between benign and malignant lesions, classifying lesions, and measuring their size and growth.11–13 Secondly, these algorithms have proven to be valuable tools in image post-processing, aiding in tasks, such as image noise reduction, correction of artefacts, and improving overall image quality.14,15 Radiographers, in particular, selected image protocol optimization as their top preference for future AI applications aids (43.3%), given its direct relevance to their professional responsibilities.
The most prevalent barriers preventing participants from learning about AI were consistent between the two groups; lack of mentorship, guidance, and support from experts (70.3%, 81.5%), lack of time to learn new technologies (70.3%, 63.7%), and lack of funding/investment in new technologies (64.4%, 72.6%) for radiologists and radiographers, respectively. Notably, more than half of the radiologists (52.5%) and radiographers (54.1%) reported not receiving any form of AI-related education. Inadequate exposure to AI concepts can result in limited knowledge about the potential benefits and applications of AI. We believe that radiologists and radiographers can expand their knowledge and understanding of AI by enrolling in specialized courses, attending conferences and webinars, and reading scholarly articles that focus on AI applications in radiology. Moreover, hospitals and AI system developers should collaborate in providing specialized training programmes specifically designed for radiologists and radiographers.
Radiology personnel face time constraints due to their demanding workloads, which restrict the time available to explore and learn about new technologies, such as AI. Lastly, the introduction and implementation of AI into radiology practice often requires significant financial investment, making the lack of funding and investment in new technologies one of the main barriers faced by radiologists and radiographers when learning about AI.
A limitation observed in previous studies was that the samples mainly consisted of participants from universities and/or teaching hospitals, potentially reflecting the opinions of participants already highly exposed to AI technology. In our study, we ensured adequate representation from all working sectors, allowing the responses to reflect the opinions and perceptions of the broader community of radiologists and radiographers in Jordan. However, our study does have limitations. First, participants who agreed to take part in the survey may have potentially had an interest in the field of AI, as maybe indicated by the relatively young age of the majority of the participants, with a mean age 38.3 for radiologists and 29.9 for radiographers. Second, because our study participants were recruited through convenience sampling, the findings might not be an accurate representation of the overall population. Third, the absence of open-ended questions in our survey, restricted the participants from elaborating on their responses and give their personal thoughts and feelings.
Following the initial introduction of AI technology in the radiology department, radiologists and radiographers will likely form a more precise assessment of AI’s abilities and limitations. Continued implementation will establish a greater comprehension of the AI systems and identification of any biases and barriers within the technology.
Conclusion
In conclusion, our study revealed that participants exhibit a positive attitude towards learning about AI and its implementation in their radiology practice. However, radiologists and radiographers reported several barriers to AI learning, with the most significant being the lack of mentorship and guidance from experts. Addressing this challenge requires a strong emphasis on AI education and training for medical imaging staff, coupled with ensuring their access to relevant resources to support their learning journey.
Future research in this area should focus on the development of tailored AI training programmes, the establishment of mentorship initiatives, and the creation of accessible resources specifically designed to meet the needs of medical imaging professionals. Additionally, evaluating the long-term impact of these interventions on the adoption and proficiency of AI in radiology practice will be crucial for advancing the field and enhancing patient care.
Supplementary Material
Acknowledgements
The authors would like to acknowledge the receipt of funding from Jordan University of Science and Technology for conducting this study.
Contributor Information
Badera Al Mohammad, Department of Allied Medical Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan.
Afnan Aldaradkeh, Department of Allied Medical Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan.
Monther Gharaibeh, Department of Special Surgery, Faculty of Medicine, The Hashemite University, Zarqa 13133, Jordan.
Warren Reed, Discipline of Medical Imaging Science, Faculty of Medicine and Health, University of Sydney 2006, Sydney, NSW, Australia.
Supplementary material
Supplementary material is available at BJR online.
Funding
This work received funding from Jordan University of Science and Technology (grant number 20230225).
Conflicts of interest
None declared.
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