A qualitative study to explore opinions of Saudi Arabian radiologists concerning AI-based applications and their impact on the future of the radiology

Abstract

Objective: The aim of this study was to explore opinions and views towards radiology AI among Saudi Arabian radiologists including both consultants and trainees. Methods: A qualitative approach was adopted, with radiologists working in radiology departments in the Western region of Saudi Arabia invited to participate in this interview-based study. Semi-structured interviews (n = 30) were conducted with consultant radiologists and trainees. A qualitative data analysis framework was used based on Miles and Huberman’s philosophical underpinnings. Results: Several factors, such as lack of training and support, were attributed to the non-use of AI-based applications in clinical practice and the absence of radiologists’ involvement in AI development. Despite the expected benefits and positive impacts of AI on radiology, a reluctance to use AI-based applications might exist due to a lack of knowledge, fear of error and concerns about losing jobs and/or power. Medical students’ radiology education and training appeared to be influenced by the absence of a governing body and training programmes. Conclusion: The results of this study support the establishment of a governing body or national association to work in parallel with universities in monitoring training and integrating AI into the medical education curriculum and residency programmes. Advances in knowledge: An extensive debate about AI-based applications and their potential effects was noted, and considerable exceptions of transformative impact may occur when AI is fully integrated into clinical practice. Therefore, future education and training programmes on how to work with AI-based applications in clinical practice may be recommended.

Medical Expectations Survey on Artificial Intelligence Solutions in daily practice

“…Our responders were mostly from the private sector, men, from different medical areas and different ages, which was important to investigate the general perception of medical AI and if physicians more recently graduated were more prone to accept AI solutions than physicians longer graduated. Most of the studies in medical AI acceptance are focused in specific areas, such as radiology (5,6,7,8), dermatology(1, 9, 10, 11) and ophthalmology (12,13,14), which could be more affected than others by adoption of AI solutions. Thus, it is important to have more general views about the topic.…”

The Use of Artificial Intelligence in Medical Imaging: A Nationwide Pilot Survey of Trainees in Saudi Arabia

“…Conversely, a study conducted in the United States reported that none of the attending radiologists and only a very small proportion of the trainees believed that their jobs would be obsolete in the next 10–20 years [ 18 ]. In Saudi Arabia, radiology professionals believe that AI can potentially change the workflow in radiology with no effect on job security [ 33 ].…”

Medical Expectations of Physicians on AI Solutions in Daily Practice: Cross-Sectional Survey Study

“…Our responders were mostly from the private sector and male and belonged to different medical areas and age groups, which was important to investigate a broader perception of medical AI. Most of the studies in medical AI acceptance were focused on specific areas, such as radiology [5][6][7][8], dermatology [1,[9][10][11], and ophthalmology [12][13][14], which could be more affected than others by the adoption of AI solutions. Thus, it is important to have more general views about the topic.…”

“…Opinion surveys are important tools in assessing satisfaction with a particular service and consist of a list of questions whose objective is to extract certain data from a group of people [8]. Previous studies on the acceptance of the use of AI in medicine were limited to specific areas, such as radiology [5,[9][10][11], dermatology [1,[12][13][14], and ophthalmology [15][16][17], as well as to specific countries. However, at the time of writing, we were unable to find studies exploring this subject among Brazilian physicians, and there are very few studies in Latin America, leaving a gap in this part of the globe.…”

“…Important questions remain regarding how machine learning interventions are being incorporated into health care [ 4 ]. A reluctance to adopt AI-based solutions might be due to a lack of knowledge; fear of error; and concerns about losing jobs, power, or both [ 5 ]. Another perceived limitation of AI applications is the belief that communication and empathy are human competencies that cannot be replaced by AI.…”