Radiologic Technology最新文献

Purpose: To explore the potential effects of a virtual reality (VR) game designed to simulate a clinical environment on the learning and skill development of radiologic technology students, focusing particularly on enhancing their understanding and execution of multidisciplinary collaboration in a clinical setting.

Methods: A VR game simulating multidisciplinary clinical scenarios was developed and deployed among 32 radiologic technology students, including 14 third-year and 18 fourth-year students. After playing the game, participants were surveyed to gather quantitative and qualitative data regarding their experiences, focusing on skill development, game functionality, and the overall learning experience.

Results: The VR tool significantly influenced students' understanding of collaborative clinical scenarios, especially in recognizing team roles and facilitating effective communication, with 65.6% affirming its efficacy in these areas. However, feedback also illuminated areas for enhancement, such as increasing game engagement and mitigating physical discomfort in the VR space, signaling essential facets for attention in future tool refinement and application.

Discussion: Student feedback highlighted an appreciation of the various roles in and collective efforts by clinical teams. However, reflecting on how these virtual experiences mirror real-world clinical scenarios is imperative. Determining whether acquired knowledge and skills during VR simulations translate into applicable, real-world competencies also is critical. Student perceptions are influenced by the ease of use and user-friendly design of VR learning tools.

Conclusion: The VR game effectively enhanced students' collaborative and communication skills and was superior to traditional lectures in providing immersive, practical experience in multidisciplinary hospital settings. Comprehensive teamwork is essential for achieving health care objectives, such as patient well-being, and VR gaming offers a promising tool for melding theoretical knowledge with practical, interprofessional collaboration.

Purpose: To explore the psychological safety of students in radiologic science programs.

Methods: A revised version of the Psychological Safety in High Fidelity Simulation scale was sent to program directors of accredited radiography, sonography, radiation therapy, magnetic resonance imaging, and nuclear medicine technology programs to share with their students. Descriptive statistics, a 1-way analysis of variance, and paired t tests were conducted to determine differences in psychological safety.

Results: A significant difference in psychological safety scores was identified between the classroom and clinical learning environments (P , .001) with students having higher levels of psychological safety in the classroom. The psychological safety scores of senior students were significantly higher than those of junior students in the classroom setting (P 5 .03) and the clinical setting (P 5 .02), and the seniors' average overall psychological safety score was significantly higher than the juniors' score (P 5 .02).

Discussion: Psychological safety differs between the classroom and clinical settings and is generally higher in the classroom. In most instances, senior students had higher psychological safety scores than did junior students.

Conclusion: Understanding the effects of student level (junior vs senior) and learning environment (classroom vs clinic) can help educators incorporate strategies to improve the psychological safety of students in radiologic science programs.

Purpose: To compare the performance of multiple large language models (LLMs) on a practice radiography certification exam.

Method: Using an exploratory, nonexperimental approach, 200 multiple-choice question stems and options (correct answers and distractors) from a practice radiography certification exam were entered into 5 LLMs: ChatGPT (OpenAI), Claude (Anthropic), Copilot (Microsoft), Gemini (Google), and Perplexity (Perplexity AI). Responses were recorded as correct or incorrect, and overall accuracy rates were calculated for each LLM. McNemar tests determined if there were significant differences between accuracy rates. Performance also was evaluated and aggregated by content categories and subcategories.

Results: ChatGPT had the highest overall accuracy of 83.5%, followed by Perplexity (78.9%), Copilot (78.0%), Gemini (75.0%), and Claude (71.0%). ChatGPT had a significantly higher accuracy rate than did Claude (P , .001) and Gemini (P 5 .02). Regarding content categories, ChatGPT was the only LLM to correctly answer all 38 patient care questions. In addition, ChatGPT had the highest number of correct responses in the areas of safety (38/48, 79.2%) and procedures (50/59, 84.7%). Copilot had the highest number of correct responses in the area of image production (43/55, 78.2%). ChatGPT also achieved superior accuracy in 4 of the 8 subcategories.

Discussion: Findings from this study provide valuable insights into the performance of multiple LLMs in answering practice radiography certification exam questions. Although ChatGPT emerged as the most accurate LLM for this practice exam, caution should be exercised when using generative artificial intelligence (AI) models. Because LLMs can generate false and incorrect information, responses must be checked for accuracy, and the models should be corrected when inaccurate responses are given.

Conclusion: Among the 5 LLMs compared in this study, ChatGPT was the most accurate model. As interest in generative AI continues to increase and new language applications become readily available, users should understand the limitations of LLMs and check responses for accuracy. Future research could include additional practice exams in other primary pathways, including magnetic resonance imaging, nuclear medicine technology, radiation therapy, and sonography.