Radiologic Technology最新文献

Purpose: To compare the ergonomic advantages of a self-driving C-arm (CIARTIC Move; Siemens Healthineers) with a standard mobile C-arm (Cios Spin; Siemens Healthineers) by evaluating physical workload and range of motion (ROM) in a simulated clinical environment.

Methods: Eight participants of varied ages, both sexes, and different levels of expertise transported equipment and performed positioning tasks while sensors collected ROM of the back and whole-body vibration (WBV) data.

Results: The CIARTIC Move significantly reduced flexion-extension ROM by 48% (P 5 .01) in younger women. Statistically significant ROM reductions with the CIARTIC Move also were observed in younger participants (36%, P 5 .02), in women (36%, P 5 .04) and in men (39%, P 5 .01). For WBV, the Cios Spin system exhibited higher and more variable acceleration values, peaking at 0.69 m/s2 (x-axis) and 0.81 m/s2 (z-axis). The CIARTIC Move decreased accelerations compared with the Cios Spin by 12% to 45% among the axes, with notable reductions in the y-axis (30%, P 5 .01) and x-axis (45%, P 5 .01). Among participants, the CIARTIC Move significantly reduced y-axis vibrations for women (33%, P 5 .03) and z-axis vibrations for men (41%, P 5 .001).

Discussion: Radiologic technologists frequently develop work-related musculoskeletal disorders (MSDs) due to repetitive tasks, awkward postures, and physical demands of operating mobile C-arms. The self-driving C-arm demonstrated superior ergonomic benefits compared with the standard model by reducing ROM and WBV among various axes and participants.

Conclusion: The CIARTIC Move might help mitigate MSD risks in radiologic technologists by promoting better motion control and decreasing physical strain.

Purpose: To examine current literature on integrating emerging technologies, artificial intelligence (AI), and informatics into medical imaging education.

Methods: A systematic review of peer-reviewed literature published in the past 5 years was conducted, focusing on medical imaging education, radiography curricula, AI applications, and ethical considerations. Articles were analyzed to identify recurring themes and trends in implementing AI and informatics in medical imaging education programs.

Results: Four key themes emerged from the literature: integration of emerging technologies and AI in medical imaging education; foundational informatics concepts and emerging technologies essential for medical imaging professionals; clinical applications of AI in medical imaging practice; and ethical and professional considerations regarding AI adoption.

Discussion: Integrating AI and informatics into medical imaging education is increasingly recognized as essential, but curriculum constraints, faculty preparedness, and the evolving nature of AI technologies are challenges to integration. Ethical concerns, including bias in AI algorithms and the potential effect on professional decision-making, highlight the need for responsible implementation. International efforts to establish AI educational frameworks are emerging that emphasize the importance of scaffolding learning to gradually build competency.

Conclusion: To ensure the safe and effective use of AI in medical imaging, structured education and professional training must be prioritized. Future research should explore best practices for AI and informatics curriculum development, standardized assessment of AI literacy, and long-term effects of AI on clinical decision-making. By addressing these areas, medical imaging professionals can remain at the forefront of technological advancements while maintaining ethical responsibility and patient-centered care.