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Background and study aim: Magnifying endoscopy enables the diagnosis of advanced neoplasia throughout the gastrointestinal tract. The unified magnifying endoscopic classification (UMEC) framework unifies optical diagnosis criteria in the esophagus, stomach, and colon, dividing lesions into three categories: non-neoplastic, intramucosal neoplasia, and deep submucosal invasive cancer. This study aims to ascertain the performance of North American endoscopists when using the UMEC. Methods: In this retrospective cohort study, five North American endoscopists without prior training in magnifying endoscopy independently diagnosed images of gastrointestinal tract lesions using UMEC. All endoscopists were blinded to endoscopic findings and histopathological diagnosis. Using histopathology as the gold standard, the endoscopists' diagnostic performances using UMEC were evaluated. Results: A total of 299 lesions (77 esophagus, 92 stomach, and 130 colon) were assessed. For esophageal squamous cell carcinoma, the sensitivity, specificity, and accuracy ranged from 65.2% (95%CI: 50.9-77.9) to 87.0% (95%CI: 75.3-94.6), 77.4% (95%CI: 60.9-89.6) to 96.8% (95%CI: 86.8-99.8), and 75.3% to 87.0%, respectively. For gastric adenocarcinoma, the sensitivity, specificity, and accuracy ranged from 94.9% (95%CI: 85.0-99.1) to 100%, 52.9% (95%CI: 39.4-66.2) to 92.2% (95%CI: 82.7-97.5), and 73.3% to 93.3%. For colorectal adenocarcinoma, the sensitivity, specificity, and accuracy ranged from 76.2% (95%CI: 62.0-87.3) to 83.3% (95%CI: 70.3-92.5), 89.7% (95%CI: 82.1-94.9) to 97.7% (95%CI: 93.1-99.6), and 86.8% to 90.7%. Intraclass correlation coefficients indicated good to excellent reliability. Conclusion: UMEC is a simple classification that may be used to introduce endoscopists to magnifying narrow-band imaging and optical diagnosis, yielding satisfactory diagnostic accuracy.
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INTRODUCTION: Focused assessment with sonography for trauma (FAST) ultrasound (US) is a valuable medical examination used in trauma settings, particularly for rapid responses to events such as natural disasters. Although the efficacy and benefits of FAST in patient care have been extensively studied, there is limited research on training medical students in FAST. Previous studies have found that medical students can proficiently perform a FAST US after two days of training. However, these studies exclusively included first-year medical students without considering variations in their medical knowledge. Particularly, the advantage of medical students having US experience before undergoing FAST training has not been previously examined. OBJECTIVES: Assess the performance and knowledge acquisition of medical students with and without prior US experience after completing a FAST training course. METHODS: The study included a total of 71 students, consisting of 33 males and 38 females, who were between the ages of 18 and 31, with an average age of 24.6 and a standard deviation of 2.4. The inclusion criteria targeted first- and second-year medical school students who participated on a volunteer basis. Students were divided into two groups: group A, consisting of those without prior US experience, and group B, made up of those who had previous US experience. All students completed a pre-training survey to share their comfort and confidence in US use and knowledge. A baseline FAST exam was conducted to establish initial performance. A comprehensive three-hour training session was then provided. Post-training, students performed another FAST exam to assess improvement, followed by a post-training survey to evaluate comfort and confidence. RESULTS: Medical students who had prior experience in the US (group B) performed significantly better (p<0.01) in both the pre- and post-training FAST exams when compared to students without previous US experience. Specifically, in locating the liver, right kidney, hepatorenal recess, and left kidney, as well as detecting fluid accumulation when in a supine position. Additionally, medical students with prior US experience (group B) exhibited higher baseline confidence (p<0.005-p<0.01) in their ability to perform a FAST exam, as indicated by the results of the pre-testing survey. CONCLUSION: Previous experience with US significantly boosted confidence and knowledge gains following FAST training. This emphasizes the value of including US training in medical school programs after earlier exposure, offering evident benefits. The study reveals the unexplored benefit of having prior US experience for medical students undergoing FAST training, thus addressing a previously unexplored area in current research. The conclusions stress the necessity of integrating US training into medical school curricula after initial exposure. This understanding can direct medical educators in refining the education process, enabling students to be better equipped for real-world medical situations involving FAST.
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OBJECTIVES: This scoping review was conducted to determine the barriers and enablers associated with the acceptance of artificial intelligence/machine learning (AI/ML)-enabled innovations into radiology practice from a physician's perspective. METHODS: A systematic search was performed using Ovid Medline and Embase. Keywords were used to generate refined queries with the inclusion of computer-aided diagnosis, artificial intelligence, and barriers and enablers. Three reviewers assessed the articles, with a fourth reviewer used for disagreements. The risk of bias was mitigated by including both quantitative and qualitative studies. RESULTS: An electronic search from January 2000 to 2023 identified 513 studies. Twelve articles were found to fulfill the inclusion criteria: qualitative studies (n = 4), survey studies (n = 7), and randomized controlled trials (RCT) (n = 1). Among the most common barriers to AI implementation into radiology practice were radiologists' lack of acceptance and trust in AI innovations; a lack of awareness, knowledge, and familiarity with the technology; and perceived threat to the professional autonomy of radiologists. The most important identified AI implementation enablers were high expectations of AI's potential added value; the potential to decrease errors in diagnosis; the potential to increase efficiency when reaching a diagnosis; and the potential to improve the quality of patient care. CONCLUSIONS: This scoping review found that few studies have been designed specifically to identify barriers and enablers to the acceptance of AI in radiology practice. The majority of studies have assessed the perception of AI replacing radiologists, rather than other barriers or enablers in the adoption of AI. To comprehensively evaluate the potential advantages and disadvantages of integrating AI innovations into radiology practice, gathering more robust research evidence on stakeholder perspectives and attitudes is essential.