This review examines how artificial intelligence (AI) and large language models (LLMs) can meet the diverse demands of obstetrics and gynecology education. Based on an exploration of their applications, benefits, and challenges, strategies are proposed for effectively integrating these emerging technologies into educational programs.Current Concepts: Traditional obstetrics and gynecology education relies on lectures, hands-on training, and clinical exposure. However, these approaches often face limitations such as restricted practical opportunities and difficulties in remaining current with rapidly evolving medical knowledge. Recent AI advancements offer enhanced data analysis and problem-solving capabilities, while LLMs, through natural language processing, can supply timely, disease-specific information and facilitate simulation-based training. Despite these benefits, concerns persist regarding data bias, ethical considerations, privacy risks, and potential disparities in healthcare access.Discussion and Conclusion: Although AI and LLMs hold promise for improving obstetrics and gynecology education by expanding access to current information and reinforcing clinical competencies, they also present drawbacks. Algorithmic transparency, data quality, and ethical use of patient information must be addressed to foster trust and effectiveness. Strengthening ethics education, developing Explainable AI, and establishing clear validation and regulatory frameworks are critical for minimizing risks such as over-diagnosis, bias, and inequitable resource distribution. When used responsibly, AI and LLMs can revolutionize obstetrics and gynecology education by enhancing teaching methods, promoting student engagement, and improving clinical preparedness.

We updated the Korean Society of Gynecologic Oncology (KSGO) practice guideline for the management of ovarian cancer as version 5.1. The ovarian cancer guideline team of the KSGO published announced the fifth version (version 5.0) of its clinical practice guidelines for the management of ovarian cancer in December 2023. In version 5.0, the selection of the key questions and the systematic reviews were based on the data available up to December 2022.Therefore, we updated the guidelines version 5.0 with newly accumulated clinical data and added 5 new key questions reflecting the latest insights in the field of ovarian cancer between 2023 and 2024. For each question, recommendation was provided together with corresponding level of evidence and grade of recommendation, all established through expert consensus.

We updated the Korean Society of Gynecologic Oncology (KSGO) practice guideline for the management of ovarian cancer as version 5.1. The ovarian cancer guideline team of the KSGO published announced the fifth version (version 5.0) of its clinical practice guidelines for the management of ovarian cancer in December 2023. In version 5.0, the selection of the key questions and the systematic reviews were based on the data available up to December 2022.Therefore, we updated the guidelines version 5.0 with newly accumulated clinical data and added 5 new key questions reflecting the latest insights in the field of ovarian cancer between 2023 and 2024. For each question, recommendation was provided together with corresponding level of evidence and grade of recommendation, all established through expert consensus.

This review examines how artificial intelligence (AI) and large language models (LLMs) can meet the diverse demands of obstetrics and gynecology education. Based on an exploration of their applications, benefits, and challenges, strategies are proposed for effectively integrating these emerging technologies into educational programs.Current Concepts: Traditional obstetrics and gynecology education relies on lectures, hands-on training, and clinical exposure. However, these approaches often face limitations such as restricted practical opportunities and difficulties in remaining current with rapidly evolving medical knowledge. Recent AI advancements offer enhanced data analysis and problem-solving capabilities, while LLMs, through natural language processing, can supply timely, disease-specific information and facilitate simulation-based training. Despite these benefits, concerns persist regarding data bias, ethical considerations, privacy risks, and potential disparities in healthcare access.Discussion and Conclusion: Although AI and LLMs hold promise for improving obstetrics and gynecology education by expanding access to current information and reinforcing clinical competencies, they also present drawbacks. Algorithmic transparency, data quality, and ethical use of patient information must be addressed to foster trust and effectiveness. Strengthening ethics education, developing Explainable AI, and establishing clear validation and regulatory frameworks are critical for minimizing risks such as over-diagnosis, bias, and inequitable resource distribution. When used responsibly, AI and LLMs can revolutionize obstetrics and gynecology education by enhancing teaching methods, promoting student engagement, and improving clinical preparedness.

We updated the Korean Society of Gynecologic Oncology (KSGO) practice guideline for the management of ovarian cancer as version 5.1. The ovarian cancer guideline team of the KSGO published announced the fifth version (version 5.0) of its clinical practice guidelines for the management of ovarian cancer in December 2023. In version 5.0, the selection of the key questions and the systematic reviews were based on the data available up to December 2022.Therefore, we updated the guidelines version 5.0 with newly accumulated clinical data and added 5 new key questions reflecting the latest insights in the field of ovarian cancer between 2023 and 2024. For each question, recommendation was provided together with corresponding level of evidence and grade of recommendation, all established through expert consensus.

This review examines how artificial intelligence (AI) and large language models (LLMs) can meet the diverse demands of obstetrics and gynecology education. Based on an exploration of their applications, benefits, and challenges, strategies are proposed for effectively integrating these emerging technologies into educational programs.Current Concepts: Traditional obstetrics and gynecology education relies on lectures, hands-on training, and clinical exposure. However, these approaches often face limitations such as restricted practical opportunities and difficulties in remaining current with rapidly evolving medical knowledge. Recent AI advancements offer enhanced data analysis and problem-solving capabilities, while LLMs, through natural language processing, can supply timely, disease-specific information and facilitate simulation-based training. Despite these benefits, concerns persist regarding data bias, ethical considerations, privacy risks, and potential disparities in healthcare access.Discussion and Conclusion: Although AI and LLMs hold promise for improving obstetrics and gynecology education by expanding access to current information and reinforcing clinical competencies, they also present drawbacks. Algorithmic transparency, data quality, and ethical use of patient information must be addressed to foster trust and effectiveness. Strengthening ethics education, developing Explainable AI, and establishing clear validation and regulatory frameworks are critical for minimizing risks such as over-diagnosis, bias, and inequitable resource distribution. When used responsibly, AI and LLMs can revolutionize obstetrics and gynecology education by enhancing teaching methods, promoting student engagement, and improving clinical preparedness.

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.

Since the latest practice guidelines for ovarian cancer were developed by the Korean Society of Gynecologic Oncology (KSGO) in 2021, many studies have examined the efficacy and safety of various treatments for epithelial ovarian cancer (EOC). Therefore, the need to develop recommendations for EOC treatments has been raised. This study searched the literature using 4 key items and the Population, Intervention, Comparison, and Outcome: the efficacy and safety of poly-ADP ribose polymerase inhibitors in newly diagnosed advanced EOC; the efficacy and safety of intraperitoneal plus intravenous chemotherapy in optimally debulked advanced EOC; the efficacy and safety of secondary cytoreductive surgery in platinumsensitive recurrent ovarian cancer; and the efficacy and safety of the addition of bevacizumab to platinum-based chemotherapy in first platinum-sensitive recurrent EOC patients who received prior bevacizumab. The evidence for these recommendations, according to each key question, was evaluated using a systematic review and meta-analysis. The committee of ovarian cancer of the KSGO developed updated guidelines for treatments of EOC.