Generative artificial intelligence (GenAI), including large language models such as GPT-4 and image-generation tools like DALL-E, is rapidly transforming the landscape of medical education. These technologies present promising opportunities for advancing personalized learning, clinical simulation, assessment, curriculum development, and academic writing. Medical schools have begun incorporating GenAI tools to support students’ self-directed study, design virtual patient encounters, automate formative feedback, and streamline content creation. Preliminary evidence suggests improvements in engagement, efficiency, and scalability. However, GenAI integration also introduces substantial challenges. Key concerns include hallucinated or inaccurate content, bias and inequity in artificial intelligence (AI)-generated materials, ethical issues related to plagiarism and authorship, risks to academic integrity, and the potential erosion of empathy and humanistic values in training. Furthermore, most institutions currently lack formal policies, structured training, and clear guidelines for responsible GenAI use. To realize the full potential of GenAI in medical education, educators must adopt a balanced approach that prioritizes accuracy, equity, transparency, and human oversight. Faculty development, AI literacy among learners, ethical frameworks, and investment in infrastructure are essential for sustainable adoption. As the role of AI in medicine expands, medical education must evolve in parallel to prepare future physicians who are not only skilled users of advanced technologies but also compassionate, reflective practitioners.

The use of artificial intelligence (AI) in thyroid ultrasound is bringing important changes to endocrine imaging, helping improve diagnostic accuracy and make the assessment of thyroid nodules more consistent. This review examines the current applications, technological approaches, clinical performance, adversities, and future directions of AI-based systems in thyroid ultrasound. Recent studies suggest that AI technologies hold significant potential in thyroid ultrasound, particularly in automated nodule detection, classification, and risk stratification. Deep learning models, particularly convolutional neural networks, achieve diagnostic accuracies exceeding 90% in distinguishing benign from malignant nodules, often matching or surpassing human radiologist performance. Current applications include Thyroid Imaging Reporting and Data System-based classification systems, lymph node metastasis prediction, and real-time diagnostic assistance. However, challenges including reproducibility concerns, clinical workflow integration, and regulatory considerations remain significant barriers to widespread adoption. While AI shows remarkable promise in thyroid ultrasound applications, challenges including validation requirements, standardization needs, and clinical integration barriers must be addressed for widespread adoption. Future developments should focus on multimodal integration, explainable AI systems, and prospective clinical trials to fully utilize the potential of AI in transforming thyroid diagnostics.

Continuous renal replacement therapy (CRRT) has become the standard modality of renal replacement therapy (RRT) in critically ill patients. However, consensus is lacking regarding the criteria for discontinuing CRRT. Here we validated the usefulness of the prediction model for successful discontinuation of CRRT in a multicenter retrospective cohort. Methods: One temporal cohort and four external cohorts included 1,517 patients with acute kidney injury who underwent CRRT for >2 days from 2018 to 2020. The model was composed of four variables: urine output, blood urea nitrogen, serum potassium, and mean arterial pressure. Successful discontinuation of CRRT was defined as the absence of an RRT requirement for 7 days thereafter. Results: The area under the receiver operating characteristic curve (AUROC) was 0.74 (95% confidence interval, 0.71–0.76). The probabilities of successful discontinuation were approximately 17%, 35%, and 70% in the low-score, intermediate-score, and highscore groups, respectively. The model performance was good in four cohorts (AUROC, 0.73–0.75) but poor in one cohort (AUROC, 0.56). In one cohort with poor performance, attending physicians primarily controlled CRRT prescription and discontinuation, while in the other four cohorts, nephrologists determined all important steps in CRRT operation, including screening for CRRT discontinuation. Conclusion: The overall performance of our prediction model using four simple variables for successful discontinuation of CRRT was good, except for one cohort where nephrologists did not actively engage in CRRT operation. These results suggest the need for active engagement of nephrologists and protocolized management for CRRT discontinuation.

Ataxia is presented by various etiologies, including acquired, genetic and degenerative disorders. Although hereditary ataxia is suspected when typical symptom of ataxia with concurrent is identified, it is sometimes difficult to diagnose hereditary ataxia without genetic test. Clinically, next generation sequencing technology has been developed and widely used for diagnosis of hereditary disease. Hereby, we experienced cases of genetically confirmed OPA1 mutation, which are presented with various clinical manifestations including ataxic gait and decreased visual acuity.

PURPOSE: In Korea, registration of paraquat-containing herbicides was canceled in November 2011, and sales thereof were completely banned in November 2012. We evaluated the effect of the paraquat ban on the epidemiology and mortality of herbicide-induced poisoning. MATERIALS AND METHODS: This retrospective study analyzed patients treated for herbicide poisoning at 17 emergency departments in South Korea between January 2010 and December 2014. The overall and paraquat mortality rates were compared pre- and post-ban. Factors associated with herbicide mortality were evaluated using logistic analysis. To determine if there were any changes in the mortality rates before and after the paraquat sales ban and the time point of any such significant changes in mortality, R software, version 3.0.3 (package, bcp) was used to perform a Bayesian change point analysis. RESULTS: We enrolled 2257 patients treated for herbicide poisoning (paraquat=46.8%). The overall and paraquat poisoning mortality rates were 40.6% and 73.0%, respectively. The decreased paraquat poisoning mortality rate (before, 75% vs. after, 67%, p=0.014) might be associated with increased intentionality. The multivariable logistic analysis revealed the paraquat ban as an independent predictor that decreased herbicide poisoning mortality (p=0.035). There were two major change points in herbicide mortality rates, approximately 3 months after the initial paraquat ban and 1 year after complete sales ban. CONCLUSION: This study suggests that the paraquat ban decreased intentional herbicide ingestion and contributed to lowering herbicide poisoning-associated mortality. The change point analysis suggests a certain timeframe was required for the manifestation of regulatory measures outcomes.
Commerce ; Eating ; Emergency Service, Hospital ; Epidemiology ; Herbicides ; Humans ; Intention ; Korea ; Mortality* ; Paraquat* ; Poisoning ; Retrospective Studies

BACKGROUND: Diet pattern of regular and three meals per day is commonly recommended. Studies investigated the health effect of gorging pattern of diet using meal frequency and meal skipping, but the health effect of meal calorie variation between three regular meals has never been investigated. In this study, maximum meal calorie variation was defined as subtraction calorie for a meal with minimum energy intake from calories for a meal with maximum energy intake between three meals and examined the effect of maximum meal calorie variation between three regular meals a day on cardiovascular risk factors. METHODS: A total of 4,680 healthy subjects aged 20-87 years who underwent medical screening examination, at one tertiary hospital health screening center and completed 24-hour dietary recall was included. Serum cholesterol subfractions, fasting glucose and blood pressure were measured. RESULTS: Maximum meal calorie variation was significantly related to serum concentration of total cholesterol (beta = 1.77; 95% confidence interval [CI], 0.36 to 3.18) and low density lipoprotein-cholesterol (LDL-C) (beta = 1.64; 95% CI, 0.37 to 2.91), body mass index (beta = 0.24; 95% CI, 0.12 to 0.37) and waist circumference (beta = 0.66; 95% CI, 0.34 to 0.98) after adjustment for potential confounders. CONCLUSION: This study suggests the notion that concentration of total cholesterol and LDL-C and obesity indices are related to maximum meal calorie variation between three meals, independently of energy intake and other confounding factors in free-living population.
Aged ; Blood Pressure ; Body Mass Index ; Cholesterol ; Diet ; Energy Intake ; Fasting ; Glucose ; Humans ; Mass Screening ; Meals ; Obesity ; Risk Factors ; Tertiary Care Centers ; Waist Circumference