Perspectives of radiographers on the emergence of artificial intelligence in diagnostic imaging in Saudi Arabia

AI may dramatically enhance the performance of health practitioners. In radiology, the transition to AI may help reduce radiographers’ workload and improve image acquisition and quality assurance. However, there is minimal research on how radiology workers in Saudi Arabia might interpret such changes. Saudi Arabia has used AI in various industries, particularly in the health sector, where there are numerous applications that chronicle the population’s health status, such as vaccines and COVID-19 infections in the pandemic. The Saudi Arabian government has established a national center for AI because it believes in its usefulness in various disciplines.

However, this technique has not yet been used in radiology. Radiology departments are undergoing a tremendous technological revolution that will markedly impact the profession [2, 11]. Before adopting this technique, it is crucial to assess radiographers’ knowledge and attitudes about AI. To the best of our knowledge, this is the first study to comprehensively assess the perspectives of radiographers from across Saudi Arabia regarding the integration of AI in radiology departments.

This survey aimed to assess Saudi radiographers’ perspectives on the implementation of AI in medical imaging. The majority of respondents (73.3%) knew that AI is an emerging trend in medical imaging, while 90.6% viewed it as the discipline’s future. This finding is similar to that of Botwe et al. [10] who reported that most participants (86.1%) agreed that AI would be the future of medical imaging. Abuzaid et al. [7] also reported that most radiographers in the Middle East and India believe that AI plays an important role in radiology. Alelyani et al. [9] also said that 61.2% of the radiological community in Saudi Arabia was aware of AI and its role in radiology. Similar excitement toward AI implementation in clinical diagnosis has also been reported by Sarwar et al. [12] who predicted a complete integration of AI within the next five years.

Regarding the positive impact of AI, most participants (72.8%) felt that it might be a helpful tool to facilitate their jobs as radiographers. This outcome will increase the number of patients examined by the MRI technician. Most respondents (65.4%) had a favorable opinion regarding the role of AI for dose optimization and image quality. Most radiographers (66.3%) felt that implementing AI in radiology departments would give them the ability to conduct research and be productive. Current findings align with those of previously published studies [3, 13]. Most respondents (93.4%) believed that the implementation of AI in radiology would improve decision-making regarding patients’ diagnostic results. The ability of AI-based decision support systems to deliver accurate diagnostic findings by triaging and flagging aberrant patient images has been reported [4, 6]. These insights are reassuring, because the issues discussed are crucial to radiography practice.

The emergence of AI in radiology raises questions about its potential impact on radiographer employment. More than half the respondents reported that the integration of AI would limit their work in the units, and a large proportion were concerned about displacement from their jobs. In addition, they even believed that radiologists’ jobs are affected by the introduction of AI in diagnostic image interpretation. Similarly, previous studies [8, 14] have found that radiologists have some concerns regarding their future job security due to the growing trends in AI technologies. The decrease in image acquisition time in MRI is an advantage of AI implementation in radiology departments. Hence, respondents seemed to agree that AI would facilitate the radiographer’s job. However, this will increase the number of daily patients examined by radiographers and thus increase the workload. This is similar to a study conducted by Botwe et al. [15] who found that radiographers agreed that the implementation of AI in medical imaging departments would “ease” their work. This perception might be influenced by arguments made in the literature that AI is expected to speed up tasks. In fact, there is some debate over whether AI would increase or decrease workload in radiology departments [16]. Many medical students do not consider radiology a future career option due to AI’s integration [17]. Although there is widespread concern that AI will replace human jobs [18], there seems to be no evidence to support this hypothesis [4]. A recent study showed that AI may be misunderstood, which may explain this belief [5].

Understanding the function of AI in medical imaging may be improved by better communication across departments and clear guidelines and policies. There was also a proportion (37.8%) of those who felt that the integration of AI would reduce their salary. It is also important to emphasize that AI cannot take the place of humans in terms of, for example, patient positioning or communication. The majority of respondents (79.8%) expressed concerns that the use of AI in radiology was associated with machine errors. Ophthalmologists and radiologists have also reported similar concerns [8, 19]. Some respondents (28.7%) were concerned about using AI tools, as this could lead to illegal utilization of patient data for inappropriate commercial purposes. This is because AI-powered devices require patient data for quality and system training [20]. However, humans who employ AI will be held responsible for avoiding these faults because AI does not integrate ethical ideas such as equality [21]. This highlights the urgent need for AI governance regulations before its deployment in Saudi Arabia.

Of note, radiographers’ perspectives on the impact of AI were not correlated with age or years of experience but rather with educational level. This might be explained by the fact that curricula for bachelor’s degrees and above contain courses on computers and programming, while the diploma curricula, although discontinued long ago, lacked computing courses. This implies that radiographers should be trained according to their educational level. However, these findings are not consistent with previous study results [10, 15]. The geographical and socioeconomic backgrounds of the current and other respondents could explain, at least in part, the differences observed in this research.

With regard to potential study limitations, we recognize that the possibility for bias in qualitative research studies is debatable. In qualitative research, bias may result from the way the question is phrased, the method by which the participants reply, and the researchers’ expectations. We did not include in our questionnaire open-ended questions that would enable participants to elaborate on their specific worries and challenges with AI, which might be considered as a limitation of this study. Another limitation of this study is that it is multicenter study in only one country. Further studies should address the international perspectives from radiographers from multiple countries.

Overall, these findings imply that radiographers working in Saudi Arabia are optimistic about implementing AI in medical imaging. However, apprehensions regarding job security are a major concern for the integration of AI in medical imaging. As with previous transformational and revolutionary technologies, the deployment of AI in medical imaging in Saudi Arabia may be difficult. Lack of expertise, regulatory laws, and support systems have been cited as significant obstacles to the effective adoption of AI, which stakeholders should address. The results indicated that radiographers struggled to obtain AI-related education and training. This difficulty is exacerbated because the radiographers have noted a shortage of post-qualification education courses. This study provides novel insights and suggestions to enhance the training of the Saudi radiography workforce and others in similar resource-limited environments to offer quality care utilizing AI-integrated imaging modalities.

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