Artificial intelligence (AI) and generative language models (GLMs) present significant opportunities for enhancing medical education, including the provision of realistic simulations, digital patients, personalized feedback, evaluation methods, and the elimination of language barriers. These advanced technologies can facilitate immersive learning environments and enhance medical students' educational outcomes. However, ensuring content quality, addressing biases, and managing ethical and legal concerns present obstacles. To mitigate these challenges, it is necessary to evaluate the accuracy and relevance of AI-generated content, address potential biases, and develop guidelines and policies governing the use of AI-generated content in medical education. Collaboration among educators, researchers, and practitioners is essential for developing best practices, guidelines, and transparent AI models that encourage the ethical and responsible use of GLMs and AI in medical education. By sharing information about the data used for training, obstacles encountered, and evaluation methods, developers can increase their credibility and trustworthiness within the medical community. In order to realize the full potential of AI and GLMs in medical education while mitigating potential risks and obstacles, ongoing research and interdisciplinary collaboration are necessary. By collaborating, medical professionals can ensure that these technologies are effectively and responsibly integrated, contributing to enhanced learning experiences and patient care.

Since manual detection of brain metastases (BMs) is time consuming, studies have been conducted to automate this process using deep learning. The purpose of this study was to conduct a systematic review and meta-analysis of the performance of deep learning models that use magnetic resonance imaging (MRI) to detect BMs in cancer patients. A systematic search of MEDLINE, EMBASE, and Web of Science was conducted until 30 September 2022. Inclusion criteria were: patients with BMs; deep learning using MRI images was applied to detect the BMs; sufficient data were present in terms of detective performance; original research articles. Exclusion criteria were: reviews, letters, guidelines, editorials, or errata; case reports or series with less than 20 patients; studies with overlapping cohorts; insufficient data in terms of detective performance; machine learning was used to detect BMs; articles not written in English. Quality Assessment of Diagnostic Accuracy Studies-2 and Checklist for Artificial Intelligence in Medical Imaging was used to assess the quality. Finally, 24 eligible studies were identified for the quantitative analysis. The pooled proportion of patient-wise and lesion-wise detectability was 89%. Articles should adhere to the checklists more strictly. Deep learning algorithms effectively detect BMs. Pooled analysis of false positive rates could not be estimated due to reporting differences.

Pretreatment CT Perfusion (CTP) parameter rCBV < 42% lesion volume has recently been shown to predict 90-day mRS. In this study, we aim to assess the relationship between rCBV < 42% and a radiographic follow-up infarct volume delineated on FLAIR images. In this retrospective evaluation of our prospectively collected database, we included acute stroke patients triaged by multimodal CT imaging, including CT angiography and perfusion imaging, with confirmed anterior circulation large vessel occlusion between 9 January 2017 and 10 January 2023. Follow-up FLAIR imaging was used to determine the final infarct volume. Student t, Mann-Whitney-U, and Chi-Square tests were used to assess differences. Spearman’s rank correlation and linear regression analysis were used to assess associations between rCBV < 42% and follow-up infarct volume on FLAIR. In total, 158 patients (median age: 68 years, 52.5% female) met our inclusion criteria. rCBV < 42% (ρ = 0.56, p < 0.001) significantly correlated with follow-up-FLAIR infarct volume. On multivariable linear regression analysis, rCBV < 42% lesion volume (beta = 0.60, p < 0.001), ASPECTS (beta = −0.214, p < 0.01), mTICI (beta = −0.277, p < 0.001), and diabetes (beta = 0.16, p < 0.05) were independently associated with follow-up infarct volume. The rCBV < 42% lesion volume is independently associated with FLAIR follow-up infarct volume.

Background: Since the closure of university campuses due to COVID-19 in spring 2020 necessitated a quick transition to online courses, medical students were isolated from hospitals and universities, negatively impacting their education. During this time, medical students had no opportunity to participate in academic discussions and were also socially isolated. Furthermore, medical doctors and professors of medical schools were given additional responsibilities during the pandemic because they were the frontliners in the fight against COVID-19. As a result, they did not have enough time to contribute effectively to medical student education. Objective: This paper describes the establishment of the Cerrahpasa Neuroscience Society Journal Clubs, a group of entirely student-run online journal clubs at Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa. Methods: The website, mass emailing, and social media accounts were used to announce the online journal clubs. Only medical students were eligible to apply. Journal clubs included psychiatry, neuroradiology, neurosurgery, neurology, and neuroscience. Following the last journal club meeting, a questionnaire created by the society’s board was distributed to the participants. SPSS Statistics (version 26) was used for statistical analysis. Results: Since March 15, 2021, synchronous online journal club meetings have been held every 2 weeks on a weekday using Google Meet, Microsoft Teams, or Zoom. Meetings of each journal club lasted approximately 1 hour on average. Interstudent interaction across multiple institutions was achieved since a total of 45 students from 11 different universities attended the meetings on a regular basis. Students on the society’s board served as academic mentors for the clubs. The clubs received excellent feedback from participants, with an overall contentment score of 4.32 out of 5. Conclusions: By establishing these clubs, we have created a venue for academic discussions, which helps to reduce the negative impact of the pandemic on education. In addition, we believe it greatly aided students in staying in touch with their peers, thereby reducing the sense of isolation. We realize that traditional journal clubs are run by faculty; however, we believe that this experience demonstrated that medical students could run a journal club on their own since the feedback from participants was excellent. Additionally, as a medical student, being a journal club academic mentor is a challenging responsibility; however, having this responsibility significantly improved our academic mentors’ leadership abilities.

Background: Collateral status (CS) is an important biomarker of functional outcomes in patients with acute ischemic stroke secondary to large vessel occlusion (AIS-LVO). Pretreatment CT perfusion (CTP) parameters serve as reliable surrogates of collateral status (CS). In this study, we aim to assess the relationship between the relative cerebral blood flow less than 38% (rCBF <38%), with the reference standard American Society of Interventional and Therapeutic Neuroradiology (ASITN) collateral score (CS) on DSA. Methods: In this prospectively collected, retrospectively reviewed analysis, inclusion criteria were as follows: (a) CT angiography (CTA) confirmed anterior circulation large vessel occlusion from 9/1/2017 to 10/01/2023; (b) diagnostic CT perfusion; and (c) underwent mechanical thrombectomy with documented ASITN CS. The ratios of the CTP-derived CBF values were calculated by dividing the values of the ischemic lesion by the corresponding values of the contralateral normal region (which were defined as rCBF). Spearman’s rank correlation and logistic regression analysis were performed to determine the relationship of rCBF <38% lesion volume with DSA ASITN CS. p ≤ .05 was considered significant. Results: In total, 223 patients [mean age: 67.77 ± 15.76 years, 56.1% ( n = 125) female] met our inclusion criteria. Significant negative correlation was noted between rCBF <38% volume and DSA CS (ρ = −0.37, p < .001). On multivariate logistic regression analysis, rCBF <38% volume was found to be independently associated with worse ASITN CS (unadjusted OR: 3.03, 95% CI: 1.60–5.69, p < .001, and adjusted OR: 2.73, 95% CI: 1.34–5.50, p < .01). Conclusion: Greater volume of tissue with rCBF <38% is independently associated with better DSA CS. rCBF <38% is a useful adjunct tool in collateralization-based prognostication. Future studies are needed to expand our understanding of the role of rCBF <38% within the decision-making in patients with AIS-LVO.