Rapid technological advances have transformed medical education, particularly in radiology, which depends on advanced imaging and visual data. Traditional electronic learning (e-learning) platforms have long served as a cornerstone in radiology education, offering rich visual content, interactive sessions, and peer-reviewed materials. They excel in teaching intricate concepts and techniques that necessitate visual aids, such as image interpretation and procedural demonstrations. However, Chat Generative Pre-Trained Transformer (ChatGPT), an artificial intelligence (AI)-powered language model, has made its mark in radiology education. It can generate learning assessments, create lesson plans, act as a round-the-clock virtual tutor, enhance critical thinking, translate materials for broader accessibility, summarize vast amounts of information, and provide real-time feedback for any subject, including radiology. Concerns have arisen regarding ChatGPT's data accuracy, currency, and potential biases, especially in specialized fields such as radiology. However, the quality, accessibility, and currency of e-learning content can also be imperfect. To enhance the educational journey for radiology residents, the integration of ChatGPT with expert-curated e-learning resources is imperative for ensuring accuracy and reliability and addressing ethical concerns. While AI is unlikely to entirely supplant traditional radiology study methods, the synergistic combination of AI with traditional e-learning can create a holistic educational experience.

A retrospective evaluation of the potential of ChatGPT in the accurate diagnosis of acute stroke - Diagnostic and Interventional Radiology

Aim: Ultrasound-guided fine-needle aspiration biopsy (FNAB) is a reliable, minimally invasive diagnostic method with high sensitivity and specificity in the evaluation of thyroid nodules. Our aim in this retrospective study was to determine if there was a difference in the adequacy ratio based on the number of needle passes in the thyroid FNABs in the absence of rapid on-site evaluation (ROSE) by the pathologist and to determine the optimal needle pass number for FNAB. Methods: Between November 2018 and February 2019, thyroid FNABs of 121 (99 female, 22 male) patients were evaluated retrospectively. Samples for each biopsy were numbered according to the order of retrieval, and 4 pairs of slides were prepared by the radiologist without on-site microscopic evaluation. Cytological results were determined according to the Bethesda classification. Results: The rate of adequacy in the first, second, third, and fourth passes were 76.0, 82.6, 77.7, and 71.2%, respectively. No statistically significant difference was found between these four groups in terms of adequacy (p = 0.21). The adequacy rates of the 1st, 1st+2nd, 1st+2nd+3rd, and cumulative evaluation of all four biopsies were 76.0, 87.6, 90.1, and 91.7%, respectively (p = 0.001). A statistically significant difference was found in the comparison of the 1st biopsy and the cumulative 1st+2nd biopsy in terms of adequacy rates (p = 0.019). However, there was no statistically significant difference between the cumulative 1st+2nd biopsy and the cumulative 1st+2nd+3rd biopsy in terms of adequacy rates (p = 0.54). Conclusions: In cases where ROSE cannot be performed, we recommend a minimum of 2 and a maximum of 3 needle entries for FNAB adequacy with the right technique and preparation.

PURPOSE This study aims to identify chest computed tomography (CT) characteristics of coronavirus disease 2019 (COVID-19), investigate the association between CT findings and laboratory or demographic findings, and compare the accuracy of chest CT with reverse transcription-polymerase chain reaction (RT-PCR). METHODS Overall, 120 of 159 consecutive cases isolated due to suspected COVID-19 at our hospital between 17 and 25 March 2020 were included in this retrospective study. All patients underwent both chest CT and RT-PCR at first admission. The patients were divided into two groups: laboratory-confirmed COVID-19 and clinically diagnosed COVID-19. Clinical findings, laboratory findings, radiologic features and CT severity index (CT-SI) of the patients were noted. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of chest CT were calculated for the diagnosis of COVID-19, using RT-PCR as reference. RESULTS The laboratory-confirmed and clinically diagnosed COVID-19 groups consisted of 69 (M/F 43/26, mean age 50.9 +/- 14.0 years) and 51 patients (M/F 24/27, mean age 50.9 +/- 18.8 years), respectively. Dry cough (62.3% vs. 52.9%), fever (30.4% vs. 25.5%) and dyspnea (23.2% vs. 27.5%) were the most common admission symptoms in the laboratory-confirmed and clinically diagnosed COVID-19 groups, respectively. Bilateral multilobe involvement (83.1% vs. 57.5%), peripheral distribution (96.9% vs. 97.5%), patchy shape (75.4% vs. 70.0%), ground-glass opacities (GGO) (96.9% vs. 100.0%), vascular enlargement (56.9% vs. 50.0%), intralobular reticular density (40.0% vs. 40.0%) and bronchial wall thickening (27.7% vs. 45.0%) were the most common CT findings in the laboratory-confirmed and clinically diagnosed COVID-19 subgroups, respectively. Except for the bilateral involvement and white blood cell (WBC) count, no difference was found between the clinical, laboratory, and parenchymal findings of the two groups. Positive correlation was found between CT-SI and, lactate dehydrogenase (LDH) and C-reactive protein (CRP) values in the laboratory-confirmed COVID-19 subgroup. Chest CT and RT-PCR positivity rates among patients with suspected COVID-19 were 87.5% (105/120) and 57.5% (69/120), respectively. The sensitivity, specificity, PPV, NPV and accuracy rates of chest CT were determined as 94.2% (95% confidence interval [CI], 85.8-98.4), 21.57% (95% CI, 11.3-35.3), 61.90% (95% CI, 58.2-65.5), 73.3% (95% CI, 48.2-89.1) and 63.3% (95% CI, 54.1-71.9), respectively. CONCLUSION Chest CT has high sensitivity and low specificity in the diagnosis of COVID-19. The clinical, laboratory, and CT findings of laboratory-confirmed and clinically diagnosed COVID-19 patients are similar.

Purpose: This study investigated interobserver agreement in lung ultrasonography (LUS) in pregnant women performed by obstetricians with different levels of expertise, with confirmation by an expert radiologist. Methods: This prospective study was conducted at a tertiary "Coronavirus Pandemic Hospital" in April 2020. Pregnant women suspected to have coronavirus disease 2019 (COVID-19) were included. Two blinded experienced obstetricians performed LUS on pregnant women separately and noted their scores for 14 lung zones. Following a theoretical and hands-on practical course, one experienced obstetrician, two novice obstetric residents, and an experienced radiologist blindly evaluated anonymized and randomized still images and videoclips retrospectively. Weighted Cohen's kappa and Krippendorff's alpha tests were used to assess the interobserver agreement. Results: Fifty-two pregnant women were included, with confirmed COVID-19 diagnosis rate of 82.7%. In total, 336 eligible still images and 115 videoclips were included in the final analysis. The overall weighted Cohen's kappa values ranged from 0.706 to 0.912 for the 14 lung zones. There were only seven instances of major disagreement (>1 point) in the evaluation of 14 lung zones of 52 patients (n=728). The overall agreement between the radiologist and obstetricians for the still images (Krippendorff's alpha=0.856, 95% confidence interval [CI] 0.797 to 0.915) and videoclips (Krippendorff's alpha=0.785; 95% CI, 0.709 to 0.861) was good. Conclusion: The interobserver agreement between obstetricians with different levels of experience on still images and videoclips of LUS was good. Following a brief theoretical course, obstetricians' performance of LUS in pregnant women and interpretation of pre-acquired LUS images can be considered consistent.