PURPOSE:To evaluate the diagnostic accuracy, clinical utility, and workflow integration of artificial intelligence (AI)-assisted sonography for the assessment of thyroid nodules and to assess the tool's potential for improving diagnostic consistency, reducing the number of unnecessary biopsies, and assisting with clinical decision-making. METHODS:Database searches were conducted to identify studies published from January 2018 to June 2025. Eligible studies compared AI-based models for thyroid sonography using histopathology or fine-needle aspiration (FNA) as reference standards. Data extraction was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 criteria. Variables included AI architecture, sonography mode or technique, study design, clinical impact measures (ie, FNA reduction, time savings), and diagnostic metrics (ie, sensitivity, specificity, and area under curve). RESULTS:A total of 30 studies were included. Convolutional neural networks were the most common architecture. Area under curve values ranged from 0.78 to 0.97, specificity from 70.4% to 90.7% and sensitivity from 75.6% to 94.0%. Clinical advantages included a 10% to 45% reduction in the number of FNAs performed, with some studies reporting between 2.5 to 3.1 minutes of interpretation time saved per case. Risk-of-bias assessment determined 53.3% of the studies to be low risk, 33.3% to be moderate risk, and 13.3% to be high risk, primarily because of the retrospective design or small sample size. DISCUSSION:Thyroid nodules are prevalent; however, interpretation of sonograms is prone to interobserver variability. Results from this systematic review indicate that AI-aided sonography exhibits high levels of diagnostic efficacy and quantifiable clinical advantages, especially when combined with the American College of Radiology Thyroid Imaging and Reporting Data System and externally validated. CONCLUSION:This review maps the evolution of experimental models to clinically implementable systems and calls for prospective multicenter trials, standardized reporting, and explainable AI to facilitate safe and reproducible usage in routine thyroid imaging. These findings support Sustainable Development Goal (SDG) 3 (good health and well-being) by promoting higher-quality diagnostic care and SDG 9 (industry, innovation, and infrastructure) by advancing AI in medical imaging.
PURPOSE:To examine mental health challenges affecting radiologic technologists and evaluate the potential role of Mental Health First Aid (MHFA) as an evidence-based strategy to support patient care, student preparedness, and workforce well-being in medical imaging departments. METHODS:A narrative review of current literature was conducted, synthesizing research on burnout, stress, and moral distress in radiologic technology alongside empirical studies evaluating MHFA outcomes in health care and educational settings. Themes were analyzed to determine the relevance and potential applicability of MHFA in radiologic technology education and clinical environments. RESULTS:Evidence demonstrates high levels of burnout, anxiety, and patient communication-related strain among radiologic technologists, particularly in high-acuity settings. MHFA training has been shown to improve mental health literacy, reduce stigma, and increase confidence in assisting individuals in distress. Simulation-based and longitudinal studies further support MHFA's effectiveness in enhancing communication and improving crisis response behaviors. DISCUSSION:These findings suggest that MHFA aligns closely with the communication, emotional, and safety demands of radiologic technologists and provides a structured framework for supporting patients and medical imaging professionals. CONCLUSION:MHFA represents a practical, scalable approach to improving well-being and communication among radiologic technologists. Incorporating MHFA into radiologic science education and departmental training might enhance patient care, support workforce retention, and foster a more resilient medical imaging profession.
PURPOSE:To evaluate geometric distortion, throughput time, and incident dose of the single-shot and rotational stitching methods to determine the potential for clinical application in long-length flat-panel detector imaging for scoliosis and lower-extremity osteoarthritis. METHODS:Three technical evaluations were performed using an anthropomorphic acrylic torso phantom in a non-weight-bearing setup. The evaluations consisted of geometric distortion, measured as apparent changes in the projected length of an x-ray ruler along the body axis; throughput time, measured in seconds from rotor initiation to completion of image acquisition; and incident dose distribution, measured at 50-mm intervals using dosimeters, with particular attention given to overlapping regions in the rotational stitching method. RESULTS:Due to geometric magnification, the total projected lengths of the physical 600-mm x-ray ruler were 612 mm for the single-shot method and 615 mm for the rotational stitching method, indicating comparable levels of geometric distortion (# 0.5% difference). The mean throughput time was found to be significantly shorter for the single-shot method (0.63 s 6 0.02 s) than for the rotational stitching method (7.36 s 6 0.08 s; P , .001). Overlapping regions exhibited incident doses approximately 2-fold higher than those exhibited by nonoverlapping regions for the rotational stitching method. Overall, the mean incident dose of the single-shot method (343 μGy) was 47.8% lower than that of the rotational stitching method (657.4 μGy), suggesting a potential advantage in radiation dose management. DISCUSSION:The 2 imaging methods showed minimal differences in geometric distortion, limited to a few millimeters. However, the single-shot method significantly reduced throughput time and patient radiation dose compared with rotational stitching, making the single-shot method a practical advantage for long-length imaging. CONCLUSION:The single-shot method provides geometric accuracy comparable with that of the rotational stitching method and significantly reduces throughput time and radiation dose, indicating its potential utility for clinical long-length imaging.
PURPOSE:To evaluate the accuracy and educational utility of Microsoft Copilot's (GPT-4, July 2025, closed-system version) responses to radiography questions through expert assessment, with a focus on strengths, limitations, and implications for radiologic science education. METHODS:This qualitative descriptive study evaluated Copilot's responses to 15 open-ended radiography questions derived from the American Registry of Radiologic Technologists Radiography Examination Content Specifications. Seven subject matter experts with extensive clinical and teaching experience independently reviewed the artificial intelligence (AI)-generated responses for accuracy and educational utility. Feedback was collected using Microsoft Forms and analyzed inductively following a 6-phase thematic analysis framework. RESULTS:Thematic analysis revealed 6 overarching themes: accuracy and completeness of content, scope of practice and role clarification, outdated terminology and standards, formatting and presentation strengths, utility as a learning aid, and need for specificity and context. Experts praised the clarity, structure, and organization of responses and noted their potential as supplemental study aids. However, concerns were raised about incomplete or superficial content, attributions outside a radiologic technologist's scope of practice, outdated terminology and standards (including shielding and grid use), lack of specificity, and insufficient clinical context. DISCUSSION:Findings suggested that although Copilot might provide structured and accessible support for radiography learners, its limitations could result in outdated or inaccurate practices if used without a critical lens. The closed-system design further constrained educational utility by preventing transparent sourcing. For radiography education, these results highlighted the importance of embedding critical AI literacy skills into curricula so that students learn to evaluate, verify, and contextualize AI-generated outputs. CONCLUSION:Copilot demonstrated potential as a supplemental learning aid in radiography education, but outdated terminology, technical inaccuracies, and lack of sourcing constrained its reliability. Future research should compare multiple AI platforms, assess student learning outcomes, and explore strategies for embedding AI literacy and institutional safeguards to support safe, effective integration into health professions education.
PURPOSE:To evaluate the factual accuracy and citation fidelity of Scopus AI's outputs in response to a single health care-related research question about the importance of human trafficking prevention education for professionals. METHODS:This study employed a mixed-methods content verification approach. A single health care-related research question was entered into Scopus AI (Elsevier), which generated a summary, expanded summary, and concept map. Quantitative data were collected by classifying each statement in the Scopus AI output as accurate, misleading, or incorrect. Qualitative analysis provided contextual insights into citation use, source type, and interpretation of content. RESULTS:Of the 30 statements analyzed from the Scopus AI output, 27 (90.0%) were rated as accurate, and 3 (10.0%) were categorized as misleading. No incorrect or hallucinated content was detected. Qualitative analysis revealed that Scopus AI consistently cited legitimate, peer-reviewed sources. However, in 2 cases, the tool referenced secondary sources without clarification, raising questions about source hierarchy. DISCUSSION:Though Scopus AI produced largely reliable academic content, this study underscores the need for user verification and scholarly judgment, particularly regarding secondary sources and citation transparency. The findings highlight the importance of teaching students to critically evaluate artificial intelligence (AI)-generated material. In response to the findings, a classroom activity titled "Fact-Check the Bot" was developed to promote critical AI literacy. This activity guides learners in assessing AI-generated claims using a verification matrix and original literature and can be adapted for use with other AI tools. CONCLUSION:This study demonstrates the potential and the limitations of generative AI in academic research and offers a model for integrating verification practices into educational settings to enhance students' critical engagement with AI tools.
