Purpose: Artificial intelligence (AI) in radiology has been a subject of heated debate. The external perception is that algorithms and machines cannot offer better diagnosis than radiologists. Reluctance to implement AI maybe due to the opacity in how AI applications work and the challenging and lengthy validation process. In this study, Saudi radiology personnel’s familiarity with AI applications and its usefulness in clinical practice were investigated. Methods: A cross-sectional study was conducted in Saudi Arabia among radiology personnel from March to April 2021. Radiology personnel nationwide were surveyed electronically using Google form. The questionnaire included 12-questions related to AI usefulness in clinical practice and participants’ knowledge about AI and their acceptance level to learn and implement this technology into clinical practice. Participants’ trust level was also measured; Kruskal–Wallis test was used to examine differences between groups. Results: A total of 224 respondents from various radiology-related occupations participated in the survey. The lowest trust level in AI applications was shown by radiologists (p = 0.033). Eighty-two percent of participants (n = 184) had never used AI in their departments. Most respondents (n = 160, 71.4%) reported lack of formal education regarding AI-based applications. Most participants (n = 214, 95.5%) showed strong interest in AI education and are willing to incorporate it into the clinical practice of radiology. Almost half of radiography students (22/46, 47.8%) believe that their job might be at risk due to AI application (p = 0.038). Conclusion: Radiology personnel’s knowledge of AI has a significant impact on their willingness to learn, use and adapt this technology in clinical practice. Participants demonstrated a positive attitude towards AI, showed a reasonable understanding and are highly motivated to learn and incorporate it into clinical practice. Some participants felt that their jobs were threatened by AI adaptation, but this belief might change with good training and education programmes.

The aim of this study was to evaluate how iterative reconstruction can compensate for the noise increase in low radiation dose abdominal computed tomography (CT) technique for large size patients and the general impact of obesity on abdominal organ doses and image quality in CT. An anthropomorphic phantom layered with either none or a single layer of 3-cm- thick circumferential animal fat packs to simulate obese patients was imaged using a 128MDCT scanner. Abdominal protocols (n = 12) were applied using automatic tube current modulation (ATCM) with various quality reference mAs (150, 200, 250 and 300). kVs of 100, 120 and 140 were used for each mAs selection. Metal oxide semiconductor field effect transistor dosimeters (MOSFET) measured internal organ dose. All images produced were reconstructed with filtered back projection (FBP) and sinogram affirmed iterative reconstruction (SAFIRE) (3, 4 and 5) and objective noise was measured within three regions of interest at the level of L4–L5. Organ doses varied from 0.12 to 41.9 mGy, the spleen received the highest doses for both phantom sizes. Compared to the phantom simulating average size, the obese phantom was associated with up to twofold increase in delivered mAs, dose length product (DLP) and computed tomography dose index (CTDIvol) for the matched mAs selection (p < 0.05). However, organ dose increased by 50% only. The use of 100 kV resulted in a 40% lower dose (p < 0.05) compared to 120 kV and the associated noise increase was improved by SAFIRE (5) use, which resulted in 60% noise reduction compared to FBP (p < 0.05). When combined with iterative reconstruction, low kV is feasible for obese patients to optimise radiation dose and maintain objective image quality.

Background: The clinical methods of diagnosing pneumonia have been for many years chest X-rays (CXR) and computed tomography (CT) scans. However, a relatively new modality that is promising, efficient, and cheap is the lung ultrasound (LUS). The scope of this systematic review focuses on evaluating the diagnostic performance of LUS, CXR, and CT for diagnosing pneumonia. Objective: The first aim of this review is to assess the diagnostic accuracy of lung ultrasound in comparison with chest X-rays and CT scans. Methods: PubMed, Cochrane, Embase, and Scopus were electronically searched without date and language limitations were set. Inclusion criteria covered only those diagnostic studies that involved comparing lung ultrasound, chest X-ray, and CT scans in human beings with suspected pneumonia. Both abstraction and quality of studies were assessed by two researchers and the quality was assessed using PRISMA and QUADAS-2 checklists. Results: There were 13 empirical studies included in the review which referred to different patients and contexts. Although none of the imaging techniques could achieve both high sensitivity and high specificity, CT scans produced the highest sensitivity, with values higher than 95% on all occasions, and specificities ranging between 80% and 90%. Lung US was most helpful in pediatric and acute care populations, whereas CT was preferred when there was uncertainty regarding the presentation. Conclusion: Lung ultrasound is a very sensitive, specific, practical method of subsequent lung examination; it is useful in environments that need fast bedside assessment and for patients particularly vulnerable to radiation exposure, such as children. Even though chest X-ray remains valuable in excluding pneumonia in outpatient practices, it lacks sensitivity and is therefore less useful in diagnosing early disease. Computed tomography scans are the most accurate type of scans but should only be used in the more severe interventions due to cost and radiation exposure.

Background: The novel Coronavirus Disease 2019 (COVID-19) pandemic has presented unparalleled and unique stressors and challenges to the field of applied health sciences education. This study explored how the College of Applied Medical Sciences (COAMS) Saudi students perceive the transition to remote/ hybrid learning during the COVID-19 pandemic.Methods: A cross-sectional exploratory investigation was carried out during the months of February and March in the year 2023 among 196- COAMS Saudi students, using the 48-item previously developed and validated questionnaire, and with a non-probability convenient sampling technique. Descriptive statistics were generated for participants’ demographics, and for each questionnaire item and statistical analysis was performed using chi-square test.Results: Out of the 283 undergraduates who have enrolled in COAMS, a total of 196 students have agreed to participate in the study with an overall response rate of 69.3%. Over 70% of COAMS students were satisfied and engaged in their on-site coursework. Nevertheless, questionnaire data indicates that their satisfaction and level of engagement diminished following the shift to remote learning. More than 62% of COAMS students were satisfied with their instructors’ instructional and assessment strategies during on-site coursework, but such perceptions have decreased with remote instruction. Hybrid learning can be beneficial and effective in improving the performance and learning experience of COAMS students. As compared to female students, COAMS male students were more satisfied with remote learning because it met their needs (p = 0.017).Conclusion: Remote classrooms have lower attendance and interest than on-site classes. Despite lower satisfaction levels in online courses, hybrid learning was viewed favourably by COAMS students. Higher educational institutions should develop plans to increase student involvement, improve academic integrity, and assess the effect of the pandemic on undergraduate education on a regular basis. By incorporating these measures, educational institutions can enhance and support the remote learning experience for their students.

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.