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Artificial Intelligence in Obstetrics: Current Applications, Opportunities, and Clinical Implementation Challenges. 产科人工智能：当前应用、机遇和临床实施挑战。
IF 1.5 4区 医学
Clinical obstetrics and gynecology Pub Date : 2026-03-01 Epub Date: 2025-12-23 DOI: 10.1097/GRF.0000000000000980
Eileen Deuster, Asma Khalil
{"title":"Artificial Intelligence in Obstetrics: Current Applications, Opportunities, and Clinical Implementation Challenges.","authors":"Eileen Deuster, Asma Khalil","doi":"10.1097/GRF.0000000000000980","DOIUrl":"10.1097/GRF.0000000000000980","url":null,"abstract":"<p><p>Artificial intelligence is transforming obstetric practice through applications in diagnostic imaging, risk prediction, and clinical decision-making. Deep learning algorithms have achieved diagnostic accuracy comparable to that of experienced clinicians. However, gaps persist between algorithmic capability and clinical implementation. Critical challenges include limited external validation and algorithmic bias. This review examines current AI applications in obstetrics across multiple clinical domains: automated fetal biometry, structural anomaly detection, prediction of pregnancy complications, and intrapartum fetal surveillance. It highlights persistent technical, ethical, and implementation barriers. Key recommendations include multicenter validation across diverse populations, explainable AI approaches, and creating strong regulatory frameworks.</p>","PeriodicalId":10415,"journal":{"name":"Clinical obstetrics and gynecology","volume":" ","pages":"54-61"},"PeriodicalIF":1.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145809785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Contributors: Artificial Intelligence in Gynecology. 贡献者：人工智能在妇科。
IF 1.4 4区 医学
Clinical obstetrics and gynecology Pub Date : 2026-03-01 Epub Date: 2026-01-23 DOI: 10.1097/01.grf.0001179584.42511.62
{"title":"Contributors: Artificial Intelligence in Gynecology.","authors":"","doi":"10.1097/01.grf.0001179584.42511.62","DOIUrl":"https://doi.org/10.1097/01.grf.0001179584.42511.62","url":null,"abstract":"","PeriodicalId":10415,"journal":{"name":"Clinical obstetrics and gynecology","volume":"69 1","pages":"v-vi"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146040553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Foreword: Artificial Intelligence in Gynecology: The Current and Future Landscape of AI in OB-GYN. 人工智能在妇产科的当前和未来前景。
IF 1.4 4区 医学
Clinical obstetrics and gynecology Pub Date : 2026-03-01 Epub Date: 2025-12-22 DOI: 10.1097/GRF.0000000000000989
Kenneth H Kim
{"title":"Foreword: Artificial Intelligence in Gynecology: The Current and Future Landscape of AI in OB-GYN.","authors":"Kenneth H Kim","doi":"10.1097/GRF.0000000000000989","DOIUrl":"10.1097/GRF.0000000000000989","url":null,"abstract":"","PeriodicalId":10415,"journal":{"name":"Clinical obstetrics and gynecology","volume":" ","pages":"1"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145803375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Contributors: Artificial Intelligence in Obstetrics. 投稿人：产科人工智能。
IF 1.4 4区 医学
Clinical obstetrics and gynecology Pub Date : 2026-03-01 Epub Date: 2026-01-23 DOI: 10.1097/01.grf.0001179588.77564.fc
{"title":"Contributors: Artificial Intelligence in Obstetrics.","authors":"","doi":"10.1097/01.grf.0001179588.77564.fc","DOIUrl":"https://doi.org/10.1097/01.grf.0001179588.77564.fc","url":null,"abstract":"","PeriodicalId":10415,"journal":{"name":"Clinical obstetrics and gynecology","volume":"69 1","pages":"vii-viii"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146040496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Artificial Intelligence for Genetic Cancer Risk Assessment in Gynecologic Oncology: A Review of the Current Landscape and Future Directions. 人工智能在妇科肿瘤遗传癌症风险评估中的应用：现状和未来发展方向综述。
IF 1.5 4区 医学
Clinical obstetrics and gynecology Pub Date : 2026-03-01 Epub Date: 2026-01-06 DOI: 10.1097/GRF.0000000000000996
Roxanna Haghighat, Sarah R Levi, Melissa K Frey
{"title":"Artificial Intelligence for Genetic Cancer Risk Assessment in Gynecologic Oncology: A Review of the Current Landscape and Future Directions.","authors":"Roxanna Haghighat, Sarah R Levi, Melissa K Frey","doi":"10.1097/GRF.0000000000000996","DOIUrl":"10.1097/GRF.0000000000000996","url":null,"abstract":"<p><p>Hereditary cancer syndromes are associated with up to 25% of ovarian and 5% of endometrial cancers, yet rates of genetic testing and counseling remain low. Artificial intelligence (AI) offers new opportunities to streamline risk assessment, enhance gene variant interpretation, and expand access to genetic counseling. This narrative review synthesizes current evidence on AI applications in gynecologic cancer genetic risk assessment, including chatbot-based risk assessment, natural language processing of electronic records, and machine-learning approaches to variant classification. We highlight key challenges, including data bias, privacy, and implementation barriers, and outline future directions for AI technologies in gynecologic cancer genetic risk assessment.</p>","PeriodicalId":10415,"journal":{"name":"Clinical obstetrics and gynecology","volume":" ","pages":"36-44"},"PeriodicalIF":1.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145910700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Enhancing Fetal Cardiac Imaging With Artificial Intelligence: A Review of the Current Evidence and Future Directions. 用人工智能增强胎儿心脏成像：当前证据和未来方向的综述。
IF 1.5 4区 医学
Clinical obstetrics and gynecology Pub Date : 2026-03-01 Epub Date: 2025-12-23 DOI: 10.1097/GRF.0000000000000992
Juliana G Martins, Rebecca Horgan, Elena Sinkovskaya
{"title":"Enhancing Fetal Cardiac Imaging With Artificial Intelligence: A Review of the Current Evidence and Future Directions.","authors":"Juliana G Martins, Rebecca Horgan, Elena Sinkovskaya","doi":"10.1097/GRF.0000000000000992","DOIUrl":"10.1097/GRF.0000000000000992","url":null,"abstract":"<p><p>Congenital heart disease (CHD) is the most common major birth anomaly and a key cause of neonatal mortality. While early diagnosis improves outcomes, prenatal detection remains inconsistent. Artificial intelligence (AI) offers scalable solutions through automation of view acquisition, image interpretation, and functional assessment. AI has shown expert-level performance in view classification, CHD detection, and cardiac biometry. Tools like Fetal Intelligent Navigation Echocardiography, though not AI, enhance consistency and efficiency. Emerging AI modalities, including generative AI, self-supervised learning, and NLP-driven report automation, expand possibilities. Ongoing research is essential to ensure safe, equitable integration of AI into clinical workflows for improved CHD diagnosis worldwide.</p>","PeriodicalId":10415,"journal":{"name":"Clinical obstetrics and gynecology","volume":" ","pages":"62-69"},"PeriodicalIF":1.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145809740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Artificial Intelligence in Gynecologic Imaging. 人工智能在妇科影像学中的应用。
IF 1.5 4区 医学
Clinical obstetrics and gynecology Pub Date : 2026-03-01 Epub Date: 2026-01-06 DOI: 10.1097/GRF.0000000000000984
Morgan Briggs, Ayesha Saif, Timothy L Kline, Wendaline VanBuren, Sarah L Cohen Rassier, Isabel C Green
{"title":"Artificial Intelligence in Gynecologic Imaging.","authors":"Morgan Briggs, Ayesha Saif, Timothy L Kline, Wendaline VanBuren, Sarah L Cohen Rassier, Isabel C Green","doi":"10.1097/GRF.0000000000000984","DOIUrl":"10.1097/GRF.0000000000000984","url":null,"abstract":"<p><p>What was done? A review of artificial intelligence (AI) applications for the imaging of uterine fibroids, endometriosis, and adenomyosis. What was found? AI models can assist with the recognition, segmentation, and localization of uterine fibroids, and the differentiation of benign fibroids and sarcomas. Models can aid in the diagnosis of adenomyosis and endometriosis, and the prediction of the impact of endometriosis on fertility. What the findings mean? Deployed thoughtfully, AI tools could reduce variability, shorten read times, and add objective measurements to routine care. Studies evaluating these models are limited by single-institution designs and continued reliance on expert sonologists and radiologists.</p>","PeriodicalId":10415,"journal":{"name":"Clinical obstetrics and gynecology","volume":"69 1","pages":"26-35"},"PeriodicalIF":1.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146040501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Summary of Artificial Intelligence in Gynecologic Surgery. 人工智能在妇科外科中的应用综述。
IF 1.5 4区 医学
Clinical obstetrics and gynecology Pub Date : 2026-03-01 Epub Date: 2026-01-06 DOI: 10.1097/GRF.0000000000000986
Grace M Pipes, Kenneth H Kim
{"title":"A Summary of Artificial Intelligence in Gynecologic Surgery.","authors":"Grace M Pipes, Kenneth H Kim","doi":"10.1097/GRF.0000000000000986","DOIUrl":"10.1097/GRF.0000000000000986","url":null,"abstract":"<p><p>Artificial intelligence is transforming healthcare, including obstetrics and gynecology, with applications for diagnostics, outcome prediction, and clinical decision support. Gynecologic surgery has begun to utilize machine learning to harness the massive datasets generated by minimally invasive gynecologic procedures to objectively assess surgical skill, enhance preoperative planning, improve intraoperative anatomy recognition, and, most importantly, correlate surgical technique with patient outcomes. This narrative review summarizes the current state of artificial intelligence in gynecologic surgery, the challenges and limitations its application faces within the field, and possible future advances within gynecologic surgical care and patient safety.</p>","PeriodicalId":10415,"journal":{"name":"Clinical obstetrics and gynecology","volume":"69 1","pages":"13-17"},"PeriodicalIF":1.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146040555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Foreword: Fetal Growth Restriction. 前言：胎儿生长限制。
IF 1.4 4区 医学
Clinical obstetrics and gynecology Pub Date : 2025-12-01 Epub Date: 2025-10-22 DOI: 10.1097/GRF.0000000000000979
Hiba J Mustafa, Asma Khalil
{"title":"Foreword: Fetal Growth Restriction.","authors":"Hiba J Mustafa, Asma Khalil","doi":"10.1097/GRF.0000000000000979","DOIUrl":"https://doi.org/10.1097/GRF.0000000000000979","url":null,"abstract":"","PeriodicalId":10415,"journal":{"name":"Clinical obstetrics and gynecology","volume":"68 4","pages":"544"},"PeriodicalIF":1.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145343808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
The Role of Angiogenic Factors in Fetal Growth Restriction. 血管生成因子在胎儿生长限制中的作用。
IF 1.5 4区 医学
Clinical obstetrics and gynecology Pub Date : 2025-12-01 Epub Date: 2025-10-07 DOI: 10.1097/GRF.0000000000000972
Helena C Bartels, Sebastian R Hobson
{"title":"The Role of Angiogenic Factors in Fetal Growth Restriction.","authors":"Helena C Bartels, Sebastian R Hobson","doi":"10.1097/GRF.0000000000000972","DOIUrl":"10.1097/GRF.0000000000000972","url":null,"abstract":"<p><p>Fetal growth restriction complicates up to 10% of pregnancies, resulting in significant morbidity and mortality. Biomarkers of placental function, such as PIGF or the sFLT-1/PlGF ratio, are increasingly used in clinical practice, as an adjunct to ultrasound, with an aim of improving detection of the at-risk fetus and reducing morbidity and mortality. Current evidence suggests this combined approach is associated with improved prediction and hence prevention of adverse outcomes such as stillbirth, as well as optimizing delivery timing. Future research should focus on determining thresholds for intervention, possible therapeutic targets for angiogenic markers and provide cost-based analysis. Despite limitations, there is growing evidence to suggest placental biomarkers play an important role in the prenatal management of high-risk pregnancies.</p>","PeriodicalId":10415,"journal":{"name":"Clinical obstetrics and gynecology","volume":" ","pages":"545-549"},"PeriodicalIF":1.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145243809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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