Objective: The performance of large language models (LLMs) and their potential utility in obstetric and gynecological education are topics of ongoing debate. This study aimed to contribute to this discussion by examining the recent advancements in LLM technology and their transformative potential in artificial intelligence. Methods: This study assessed the performance of generative pre-trained transformer (GPT)-3.5 and -4 in understanding clinical information, as well as its potential implications for obstetric and gynecological education. Obstetrics and gynecology residents at three hospitals underwent an annual promotional examination, from which 116 of the 170 questions over 4 years (2020-2023) were analyzed, excluding 54 questions with images. The scores achieved by GPT-3.5, -4, and the 100 residents were compared. Results: The average scores across all 4 years for GPT-3.5 and -4 were 38.79 (standard deviation [SD], 5.65) and 79.31 (SD, 3.67), respectively. For groups first-year resident, second-year resident, and third-year resident, the cumulative annual average scores were 79.12 (SD, 9.00), 80.95 (SD, 5.86), and 83.60 (SD, 6.82), respectively. No statistically significant differences were observed between the scores of GPT-4.0 and those of the residents. When analyzing questions specific to obstetrics, the average scores for GPT-3.5 and -4.0 were 33.44 (SD, 10.18) and 90.22 (SD, 7.68), respectively. Conclusion GPT-4 demonstrated exceptional performance in obstetrics, different types of data interpretation, and problem solving, showcasing the potential utility of LLMs in these areas. However, acknowledging the constraints of LLMs is crucial and their utilization should augment human expertise and discernment.

Objective: The performance of large language models (LLMs) and their potential utility in obstetric and gynecological education are topics of ongoing debate. This study aimed to contribute to this discussion by examining the recent advancements in LLM technology and their transformative potential in artificial intelligence. Methods: This study assessed the performance of generative pre-trained transformer (GPT)-3.5 and -4 in understanding clinical information, as well as its potential implications for obstetric and gynecological education. Obstetrics and gynecology residents at three hospitals underwent an annual promotional examination, from which 116 of the 170 questions over 4 years (2020-2023) were analyzed, excluding 54 questions with images. The scores achieved by GPT-3.5, -4, and the 100 residents were compared. Results: The average scores across all 4 years for GPT-3.5 and -4 were 38.79 (standard deviation [SD], 5.65) and 79.31 (SD, 3.67), respectively. For groups first-year resident, second-year resident, and third-year resident, the cumulative annual average scores were 79.12 (SD, 9.00), 80.95 (SD, 5.86), and 83.60 (SD, 6.82), respectively. No statistically significant differences were observed between the scores of GPT-4.0 and those of the residents. When analyzing questions specific to obstetrics, the average scores for GPT-3.5 and -4.0 were 33.44 (SD, 10.18) and 90.22 (SD, 7.68), respectively. Conclusion GPT-4 demonstrated exceptional performance in obstetrics, different types of data interpretation, and problem solving, showcasing the potential utility of LLMs in these areas. However, acknowledging the constraints of LLMs is crucial and their utilization should augment human expertise and discernment.

Objective: The performance of large language models (LLMs) and their potential utility in obstetric and gynecological education are topics of ongoing debate. This study aimed to contribute to this discussion by examining the recent advancements in LLM technology and their transformative potential in artificial intelligence. Methods: This study assessed the performance of generative pre-trained transformer (GPT)-3.5 and -4 in understanding clinical information, as well as its potential implications for obstetric and gynecological education. Obstetrics and gynecology residents at three hospitals underwent an annual promotional examination, from which 116 of the 170 questions over 4 years (2020-2023) were analyzed, excluding 54 questions with images. The scores achieved by GPT-3.5, -4, and the 100 residents were compared. Results: The average scores across all 4 years for GPT-3.5 and -4 were 38.79 (standard deviation [SD], 5.65) and 79.31 (SD, 3.67), respectively. For groups first-year resident, second-year resident, and third-year resident, the cumulative annual average scores were 79.12 (SD, 9.00), 80.95 (SD, 5.86), and 83.60 (SD, 6.82), respectively. No statistically significant differences were observed between the scores of GPT-4.0 and those of the residents. When analyzing questions specific to obstetrics, the average scores for GPT-3.5 and -4.0 were 33.44 (SD, 10.18) and 90.22 (SD, 7.68), respectively. Conclusion GPT-4 demonstrated exceptional performance in obstetrics, different types of data interpretation, and problem solving, showcasing the potential utility of LLMs in these areas. However, acknowledging the constraints of LLMs is crucial and their utilization should augment human expertise and discernment.

Objective: The performance of large language models (LLMs) and their potential utility in obstetric and gynecological education are topics of ongoing debate. This study aimed to contribute to this discussion by examining the recent advancements in LLM technology and their transformative potential in artificial intelligence. Methods: This study assessed the performance of generative pre-trained transformer (GPT)-3.5 and -4 in understanding clinical information, as well as its potential implications for obstetric and gynecological education. Obstetrics and gynecology residents at three hospitals underwent an annual promotional examination, from which 116 of the 170 questions over 4 years (2020-2023) were analyzed, excluding 54 questions with images. The scores achieved by GPT-3.5, -4, and the 100 residents were compared. Results: The average scores across all 4 years for GPT-3.5 and -4 were 38.79 (standard deviation [SD], 5.65) and 79.31 (SD, 3.67), respectively. For groups first-year resident, second-year resident, and third-year resident, the cumulative annual average scores were 79.12 (SD, 9.00), 80.95 (SD, 5.86), and 83.60 (SD, 6.82), respectively. No statistically significant differences were observed between the scores of GPT-4.0 and those of the residents. When analyzing questions specific to obstetrics, the average scores for GPT-3.5 and -4.0 were 33.44 (SD, 10.18) and 90.22 (SD, 7.68), respectively. Conclusion GPT-4 demonstrated exceptional performance in obstetrics, different types of data interpretation, and problem solving, showcasing the potential utility of LLMs in these areas. However, acknowledging the constraints of LLMs is crucial and their utilization should augment human expertise and discernment.

Objective: The performance of large language models (LLMs) and their potential utility in obstetric and gynecological education are topics of ongoing debate. This study aimed to contribute to this discussion by examining the recent advancements in LLM technology and their transformative potential in artificial intelligence. Methods: This study assessed the performance of generative pre-trained transformer (GPT)-3.5 and -4 in understanding clinical information, as well as its potential implications for obstetric and gynecological education. Obstetrics and gynecology residents at three hospitals underwent an annual promotional examination, from which 116 of the 170 questions over 4 years (2020-2023) were analyzed, excluding 54 questions with images. The scores achieved by GPT-3.5, -4, and the 100 residents were compared. Results: The average scores across all 4 years for GPT-3.5 and -4 were 38.79 (standard deviation [SD], 5.65) and 79.31 (SD, 3.67), respectively. For groups first-year resident, second-year resident, and third-year resident, the cumulative annual average scores were 79.12 (SD, 9.00), 80.95 (SD, 5.86), and 83.60 (SD, 6.82), respectively. No statistically significant differences were observed between the scores of GPT-4.0 and those of the residents. When analyzing questions specific to obstetrics, the average scores for GPT-3.5 and -4.0 were 33.44 (SD, 10.18) and 90.22 (SD, 7.68), respectively. Conclusion GPT-4 demonstrated exceptional performance in obstetrics, different types of data interpretation, and problem solving, showcasing the potential utility of LLMs in these areas. However, acknowledging the constraints of LLMs is crucial and their utilization should augment human expertise and discernment.

Objective: This study evaluated the efficacy and effectiveness of a new patient handover system developed for better handover in a metropolitan emergency department (ED). Methods: A retrospective observational study was designed to evaluate the appropriateness and satisfaction level of the new ED handover system. The participants were pre-hospital emergency medical service (EMS) providers with patient transport experience before and after the pilot of the new handover system. Results: A questionnaire was completed by 37 pre-hospital EMS providers who transported patients to the emergency department. Based on the results, pre-hospital EMS providers felt an increased level of kindness from the ED healthcare professionals during patient handover (P<0.001), from 3.19±1.05 points before the introduction of the system to 3.97±0.96 points after its introduction, and the activeness of ED healthcare professionals also increased, from 3.35±1.03 to 4.14±0.86 points (P<0.001). The sufficiency of contents of patient handover information to explain a patient’s condition increased from 3.59±0.76 to 4.08±0.72 points (P<0.003). The score for overall satisfaction felt by the EMS providers during patient handover increased from 3.46±0.96 to 3.76±0.86 points, which was not statistically significant (P=0.020). Conclusion Our findings suggest that the introduction of a new patient handover system between EMS providers and the ED staff is effective for both pre-hospital EMS providers and ED staff.

Background: To determine the effects of Jerusalem Artichoke extract (JAE) and inulin on blood glucose levels and insulin secretion in streptozotocin (STZ)-induced diabetic mice.
Methods: Thirty four mice were divided into a normal control group and three experimental groups: diabetic control, JAE, and inulin. STZ (50 mg/kg) was injected intraperitoneally to induce diabetes in the three experimental groups. The JAE and inulin groups were fed 10 g/kg JAE or fed 1 g/kg inulin, respectively, for 6 weeks. Fasting glucose was checked weekly. After 6 weeks, the oral glucose tolerance test (OGTT) was performed, and the insulin level was checked.
Results: Four mice from the JAE group (n = 9) died and autopsies revealed inflammation and ulceration of skin lesions on the chest areas. Fasting glucose levels were not decreased in the inulin or JAE group relative to diabetic control group. In the OGTT at 60 minutes and 120 minutes, the serum glucose levels were significantly higher in the inulin group (572.6 ± 52.0 mg/dL and 555.8 ± 72.9 mg/dL, respectively) than in diabetic control group (484.3 ± 81.6 mg/dL and 467.3 ± 111.1 mg/dL, respectively). Insulin levels were not increased in the inulin group relative to the diabetic control group.
Conclusion These results indicate that JAE and inulin might not be useful therapeutic strategies for diabetes mellitus and indiscreet intake of Jerusalem Artichoke could exacerbate to diabetes.

The year 2020 marks the 112th year of the Korean Medical Association (KMA), which is a historic organization of medical experts. Since its foundation the KMA has contributed to the promotion of the health and medical care environment as well as the establishment and development of related policies. In times of health and medical care crises in the country, the KMA has always fought at the front lines. However, recent internal conflicts in the medical community have caused a lack of consistency and persistence in responding to or pursuing various health and medical policies. It weakens the KMA’s social status and influence, raising demands for its improvement. The first step for the betterment of the KMA is to analyze its critical situation. This study assumes that the internal conflicts are caused by the KMA’s governance. Through an analysis of how the KMA is currently governed, this study highlights the problems and suggests a direction for improvement.

Bazedoxifene, used as bazedoxifene acetate, is a selective estrogen receptor modulator that selectively affects the uterus, breast tissue, bone metabolism, and lipid metabolism by antagonizing or enhancing estrogens in the estrogen receptor in the tissue. This study was conducted as an open, randomized, two-period, two-treatment, crossover design to compare the pharmacokinetic (PK) characteristics and tolerability of two bazedoxifene tablets when administered to 50 healthy Korean male volunteers. Enrolled subjects were randomly allocated to 2 sequences of a single oral administration of a test drug and a reference drug, or vice versa with a 14-day washout period between the two doses. Serial blood samples were collected over 96 h for PK analysis. Plasma concentration of bazedoxifene was assayed using liquid chromatography-tandem spectrometry mass. Forty-five participants completed the study with no clinically relevant safety issues. The peak concentrations (Cmax, mean ± strandard deviation) of reference drug and test drug were 3.191 ± 1.080 and 3.231 ± 1.346 ng/mL, respectively, and the areas under the plasma concentration‐time curve from 0 to the last measurable concentration (AUClast) were 44.697 ± 21.168 ng∙h/mL and 45.902 ± 23.130 ng∙h/mL, respectively. The geometric mean ratios of test drug to reference drug and their 90% confidence intervals for Cmax and AUClast were 0.9913 (0.8828–1.1132) and 1.0106 (0.9345–1.0929), respectively. The incidence of adverse events between the two formulations was similar. The present study showed that PK and tolerability of two bazedoxifene tablet formulations were comparable when administered to healthy Korean male volunteers.

PURPOSE: With the emergence of next-generation sequencing (NGS) technology, profiling a wide range of genomic alterations has become a possibility resulting in improved implementation of targeted cancer therapy. In Asian populations, the prevalence and spectrum of clinically actionable genetic alterations has not yet been determined because of a lack of studies examining high-throughput cancer genomic data. MATERIALS AND METHODS: To address this issue, 1,071 tumor samples were collected from five major cancer institutes in Korea and analyzed using targeted NGS at a centralized laboratory. Samples were either fresh frozen or formalin-fixed, paraffin embedded (FFPE) and the quality and yield of extracted genomic DNA was assessed. In order to estimate the effect of sample condition on the quality of sequencing results, tissue preparation method, specimen type (resected or biopsied) and tissue storage time were compared. RESULTS: We detected 7,360 non-synonymous point mutations, 1,164 small insertions and deletions, 3,173 copy number alterations, and 462 structural variants. Fifty-four percent of tumors had one or more clinically relevant genetic mutation. The distribution of actionable variants was variable among different genes. Fresh frozen tissues, surgically resected specimens, and recently obtained specimens generated superior sequencing results over FFPE tissues, biopsied specimens, and tissues with long storage duration. CONCLUSION: In order to overcome, challenges involved in bringing NGS testing into routine clinical use, a centralized laboratory model was designed that could improve the NGS workflows, provide appropriate turnaround times and control costs with goal of enabling precision medicine.
Academies and Institutes ; Asian Continental Ancestry Group ; DNA ; Humans ; Korea ; Methods ; Paraffin ; Point Mutation ; Precision Medicine ; Prevalence