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.

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.

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

The molecular mechanism underlying the initiation of somatic cell reprogramming into induced pluripotent stem cells (iPSCs) has not been well described. Thus, we generated single-cell-derived clones by using a combination of drug-inducible vectors encoding transcription factors (Oct4, Sox2, Klf4 and Myc) and a single-cell expansion strategy. This system achieved a high reprogramming efficiency after metabolic and epigenetic remodeling. Functional analyses of the cloned cells revealed that extracellular signal-regulated kinase (ERK) signaling was downregulated at an early stage of reprogramming and that its inhibition was a driving force for iPSC formation. Among the reprogramming factors, Myc predominantly induced ERK suppression. ERK inhibition upregulated the conversion of somatic cells into iPSCs through concomitant suppression of serum response factor (SRF). Conversely, SRF activation suppressed the reprogramming induced by ERK inhibition and negatively regulated embryonic pluripotency by inducing differentiation via upregulation of immediate early genes, such as c-Jun, c-Fos and EGR1. These data reveal that suppression of the ERK-SRF axis is an initial molecular event that facilitates iPSC formation and may be a useful surrogate marker for cellular reprogramming.

OBJECTIVE: To evaluate the effectiveness of intensive neurodevelopmental treatment (NDT) on gross motor function for the children having developmental delay (DD), with or without cerebral palsy (CP). METHODS: Forty-two children had intensive NDT three times weekly, 60 minutes a day, for 3 months, immediately followed by conventional NDT once or twice a week, 30 minutes a day, for another 3 months. We assessed Gross Motor Function Measure (GMFM) over three time points: before conventional NDT, before and after intensive NDT, and after 3 months of additional conventional NDT. RESULTS: The GMFM score in DD children significantly improved after intensive NDT, and the improvement maintained after 3 months of conventional NDT (p<0.05). The children were further divided into two groups: DD with CP and DD without CP. Both groups showed significant improvement and maintained the improvements, after intensive NDT (p<0.05). Also, there was no significant difference in treatment efficacy between the two groups. When we calculate the absence rate for comparing the compliance between intensive and conventional NDT, the absence rate was lower during the intensive NDT. CONCLUSION: Intensive NDT showed significantly improved gross motor function and higher compliance than conventional NDT. Additionally, all improvements were maintained through subsequent short-term conventional NDT. Thus, we recommend the intensive NDT program by day-hospital centers for children with DD, irrespective of accompanying CP.
Cerebral Palsy* ; Child* ; Compliance ; Developmental Disabilities ; Disability Evaluation ; Humans ; Rehabilitation ; Treatment Outcome