Total exposure to ionizing radiation has nearly doubled in the last two decades. This increase is primarily due to increased computed tomography (CT) exposure. Concerns have been raised about the risks associated with patients' exposure to medical imaging radiation, which can increase a person's lifetime risk of developing cancer. Preventing unnecessary examinations becomes critical at this point. To avoid unnecessary examinations, it is necessary to understand the demanding process. To ascertain clinicians' awareness of and reasons for requesting a CT examination. We developed an online questionnaire that included 20 questions about clinicians' awareness of radiation safety and their reasons for requesting a CT examination, as well as demographic information such as age, gender, and year of medical practice experience. Additionally, we asked participants the number of CT scans requested in a month, the patients' questions and approaches about the imaging method, the effect of the patient's previous imaging history on the current imaging request, whether they believed that they had sufficient information about radiation doses, and whether they requested CT without an indication. We administered the questionnaire to clinicians from a variety of different professions in four different cities. A total of 195 clinicians participated. Internal medicine specialists were the most crowded group (38/195, 19.5%). Mean age of the population was 33.66 ± 5.92 years. Mean year of experience was 9.01 ± 5.96. Mean number of requested CT scans in a month was 36.88 ± 5.86. Forty-five (23.1%) participants stated that they requested CT scans without clinical indication. The most common reasons for CT scan requests were work load, fear of malpractice, and patient demand/insistence. CT scan requests are influenced by a variety of factors, both internal and external to the doctors and patients. Raising awareness of radiation safety and reducing fear of malpractice by limiting the number of patients per physician may result in a reduction in unnecessary CT examinations and ionizing radiation exposure.

Earthquakes are among the most destructive and unpredictable natural disasters. Various diseases and ailments, such as bone fractures, organ and soft-tissue injuries, cardiovascular diseases, lung diseases, and infectious diseases, can develop in the aftermath of severe earthquakes. Digital radiography, ultrasound, computed tomography, and magnetic resonance imaging are significant imaging modalities utilized for the quick and reliable assessment of earthquake-related ailments to facilitate the planning of suitable therapy. This article examines the usual radiological imaging characteristics observed in individuals from quake-damaged regions and summarizes the strengths and functionality of imaging modalities. In such circumstances, where quick decision-making processes are life-saving and essential, we hope this review will be a practical reference for readers.

Background/Objectives: The role of artificial intelligence (AI) in radiological image analysis is rapidly evolving. This study evaluates the diagnostic performance of Chat Generative Pre-trained Transformer Omni (GPT-4 Omni) in detecting intracranial hemorrhages (ICHs) in non-contrast computed tomography (NCCT) images, along with its ability to classify hemorrhage type, stage, anatomical location, and associated findings. Methods: A retrospective study was conducted using 240 cases, comprising 120 ICH cases and 120 controls with normal findings. Five consecutive NCCT slices per case were selected by radiologists and analyzed by ChatGPT-4o using a standardized prompt with nine questions. Diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated by comparing the model’s results with radiologists’ assessments (the gold standard). After a two-week interval, the same dataset was re-evaluated to assess intra-observer reliability and consistency. Results: ChatGPT-4o achieved 100% accuracy in identifying imaging modality type. For ICH detection, the model demonstrated a diagnostic accuracy of 68.3%, sensitivity of 79.2%, specificity of 57.5%, PPV of 65.1%, and NPV of 73.4%. It correctly classified 34.0% of hemorrhage types and 7.3% of localizations. All ICH-positive cases were identified as acute phase (100%). In the second evaluation, diagnostic accuracy improved to 73.3%, with a sensitivity of 86.7% and a specificity of 60%. The Cohen’s Kappa coefficient for intra-observer agreement in ICH detection indicated moderate agreement (κ = 0.469). Conclusions: ChatGPT-4o shows promise in identifying imaging modalities and ICH presence but demonstrates limitations in localization and hemorrhage type classification. These findings highlight its potential for improvement through targeted training for medical applications.

Emergency radiology has evolved into a significant subspecialty over the past 2 decades, facing unique challenges including escalating imaging volumes, increasing study complexity, and heightened expectations from clinicians and patients. This review provides a comprehensive overview of the key requirements for an effective emergency radiology unit. Emergency radiologists play a crucial role in real-time decision-making by providing continuous 24/7 support, requiring expertise across various organ systems and close collaboration with emergency physicians and specialists. Beyond image interpretation, emergency radiologists are responsible for organizing staff schedules, planning equipment, determining imaging protocols, and establishing standardized reporting systems. Operational considerations in emergency radiology departments include efficient scheduling models such as circadian-based scheduling, strategic equipment organization with primary imaging modalities positioned near emergency departments, and effective imaging management through structured ordering systems and standardized protocols. Preparedness for mass casualty incidents requires a well-organized workflow process map detailing steps from patient transfer to image acquisition and interpretation, with clear task allocation and imaging pathways. Collaboration between emergency radiologists and physicians is essential, with accurate communication facilitated through various channels and structured reporting templates. Artificial intelligence has emerged as a transformative tool in emergency radiology, offering potential benefits in both interpretative domains (detecting intracranial hemorrhage, pulmonary embolism, acute ischemic stroke) and non-interpretative applications (triage systems, protocol assistance, quality control). Despite implementation challenges including clinician skepticism, financial considerations, and ethical issues, AI can enhance diagnostic accuracy and workflow optimization. Teleradiology provides solutions for staff shortages, particularly during off-hours, with hybrid models allowing radiologists to work both on-site and remotely. This review aims to guide stakeholders in establishing and maintaining efficient emergency radiology services to improve patient outcomes.

The aim of this study was to quantitatively assess supraspinatus tendon pathologies with T2/T2* mapping techniques, which are sensitive to biochemical changes. Conventional magnetic resonance imaging (MRI) and T2/T2* mapping techniques were applied to 41 patients with shoulder pathology, and there were also 20 asymptomatic cases included. The patients were divided into two groups: tendinosis and rupture. The supraspinatus tendon was divided into medial, middle, and lateral sub-regions, and the T2/T2* values were measured in both the coronal and sagittal planes for intergroup comparison. Intra-class and inter-class correlation coefficients (ICCs) were calculated to assess test reproducibility. Receiver operating characteristic (ROC) analysis was used to determine the cut-off value in each group. A total of 61 patients (27 males and 34 females)—including 20 asymptomatic individuals, 20 with tendinosis, and 21 with rupture—were evaluated using T2/T2* mapping techniques. In the rupture group, there were significant differences in the values of the lateral region (p < 0.001), as well as in the middle and medial regions (p < 0.05) of the supraspinatus tendon compared to the tendinosis and asymptomatic groups. These were determined using both T2* and T2 mapping in both the coronal and sagittal plane measurements. In the tendinosis group, there were significant differences in the values of the lateral region with T2* mapping (p < 0.001) in both the coronal and sagittal planes, and also with the T2 mapping in the coronal plane (p < 0.05) compared to the asymptomatic groups. The cut-off values for identifying supraspinatus pathology ranged from 85% to 90% for T2 measurements and above 90% for T2* measurements in both planes of the lateral section. The ICC values showed excellent reliability (ICC > 0.75) for all groups. In conclusion, T2 and T2* mapping techniques with 1.5 T MRI can be used to assess tendon rupture and tendinosis pathologies in the supraspinatus tendon. For an accurate evaluation, measurements from the lateral region in both the coronal and sagittal planes are more decisive.

Shear wave elastography (SWE) is a non-invasive imaging technique used to quantify the elasticity/stiffness of any tissue. There are normative SWE studies on tonsils in healthy children in the literature. The purpose of this study is to analyze the palatine tonsils in children with acute tonsillitis using ultrasound and SWE. In this prospective study, pediatric patients aged 4–18 years diagnosed with acute tonsillitis and healthy children were included. Those with antibiotic use, chronic tonsillitis, adenoid hypertrophy, and having chronic disease, immunodeficiency, and autoimmune disease, or any rheumatological disease were excluded. The volume and elasticity of palatine tonsil were measured via ultrasound and SWE. The study included 81 (46 female, 35 male) acute tonsillitis patients, and 63 (38 female, 25 male) healthy children between the ages of 4 and 18. Elasticity (kPa) values of tonsils were found significantly higher in the tonsillitis group (SWE-R: 25.39 ± 4.64, SWE-L: 25.01 ± 4.17) compared to the normal group (SWE-R: 9.71 ± 2.37, SWE-L: 9.39 ± 2.19) (p < 0.001). In the tonsillitis group, a significant positive correlation was found between tonsil volume and elasticity (r: 0.774, p: 0.002). In conclusion, in pediatric patients with acute tonsillitis, higher kPa values were obtained with SWE in the palatine tonsils.