Research trends of global artificial intelligence application in obstetrics and gynecology from 1999 to 2025: a bibliometric analysis based on web of science.

As the search period extended to June 24, 2025, the data for 2025 remain incomplete, and the publication volume (112 articles) does not fully represent the entire Year. Therefore, an annual trend analysis was conducted for AI applications in obstetrics and gynecology from January 1, 1999, to June 24, 2025, as shown in Fig.  2 . The analysis revealed that the annual publication volume generally exhibited an upward trend, closely following a linear growth model (R2 = 0.8995). The peak in publication output occurred in 2024, with 204 articles published. Furthermore, the average annual output from 2020 to 2024 was 118.2 articles, indicating a consistent increase in scholarly contributions to AI applications in obstetrics and gynecology, underscoring the promising future potential of this research domain. Fig. 2 Annual output of AI in obstetrics and gynecology research Annual output of AI in obstetrics and gynecology research A total of 79 countries and regions have contributed to the research on AI applications in obstetrics and gynecology. China leads in publication output with 283 papers, followed by the United States with 228. The United States also has the highest total citation count at 5,995, followed by Canada with 2,245 citations. Among the top ten most productive countries and regions, Canada exhibits the highest average citations per publication (citation count/publication volume) at 49.8889, with the United States following at 26.2939. Detailed data are presented in Table  1 and Fig.  3 . The country/region collaboration network is depicted in Fig.  4 . High-output nations in this field are primarily located in North America, Asia, and Europe, with well-established collaborative networks. The geographical visualization of international collaboration is shown in Fig.  5 . Table 1 Top ten countries/regions in terms of number of publications Rank Country No. of documents Total citations Average citations per article 1 China 283 1876 6.629 2 USA 228 5995 26.2939 3 England 78 2033 26.0641 4 Italy 53 1208 22.7925 5 Spain 49 761 15.5306 6 Canada 45 2245 49.8889 7 South Korea 44 434 9.8636 8 France 33 298 9.0303 9 Germany 33 522 15.8182 10 Israel 32 302 9.4375 Fig. 3 Distribution of the top ten countries/regions in terms of the number of articles published Fig. 4 Countries/regions co-authorship analysis map Fig. 5 World map showing the total number of publications by country/region Top ten countries/regions in terms of number of publications Distribution of the top ten countries/regions in terms of the number of articles published Countries/regions co-authorship analysis map World map showing the total number of publications by country/region The number of published papers is a key indicator for evaluating scientific research capacity and reflects the academic strength of research institutions. Based on the retrieved literature, as of June 2025, 331 institutions have contributed to AI research in obstetrics and gynecology. The top ten most productive institutions by publication output include the University of California System, Harvard University, Harvard University Medical Affiliates, University of Texas System, Catholic University of the Sacred Heart, IRCCS Policlinico Gemelli, Hebrew University of Jerusalem, Shanghai Jiao Tong University, Fudan University, and the Institut National de la Santé et de la Recherche Médicale (Inserm), as detailed in Table  2 . Each of these institutions has published more than ten articles in this domain. The University of California System leads in total publication count, underscoring its prominent role in this research field. The University of Texas System holds the highest centrality value, indicating its critical position within the collaboration network. The institutional collaboration network, shown in Fig.  6 , highlights the established partnerships among these institutions. However, there remains significant potential to strengthen collaboration, especially through enhancing cross-border partnerships, which could greatly accelerate progress in this area of research. Table 2 Top ten institutions by number of publications Rank Affiliation Freq Centrality Country 1 University of California System 27 0.05 USA 2 Harvard University 22 0.06 USA 3 Harvard University Medical Affiliates 20 0.04 USA 4 University of Texas System 19 0.32 USA 5 Catholic University of the Sacred Heart 15 0.10 Italy 6 IRCCS Policlinico Gemelli 15 0.10 Italy 7 Hebrew University of Jerusalem 14 0.06 Israel 8 Shanghai Jiao Tong University 14 0.10 China 9 Fudan University 14 0.09 China 10 Institut National de la Sante et de la Recherche Medicale (Inserm) 14 0.09 France Fig. 6 A co-occurrence analysis map of institutions in the field of AI in obstetrics and gynecology Top ten institutions by number of publications A co-occurrence analysis map of institutions in the field of AI in obstetrics and gynecology Price’s Law was applied to calculate the threshold for identifying core authors using the formula N = 0.749 × (ηmax)1/2, where ηmax represents the number of publications by the most prolific author. Authors publishing more than N papers are classified as core authors. The calculated threshold value, N = 5.243, establishes the minimum requirement of 6 publications to be considered a core author in this field. Since 1999, 27 core authors have been identified, contributing 227 publications, which account for 24.5% of the total literature in this domain. This relatively low proportion suggests limited contributions from the core author group to AI research in obstetrics and gynecology. Table 3 presents the top ten most prolific authors based on the author co-occurrence map generated by CiteSpace. Laios, Alexandros has the highest publication output, reflecting a significant scholarly contribution to the field. However, all authors show a centrality value of zero, indicating the lack of dominant figures within the current research network. Figure  7 depicts the collaboration network among authors, revealing sparse and loosely connected ties, with most researchers working independently and no prominent research teams established. These findings highlight substantial opportunities to strengthen collaboration among authors and research groups. Figure  8 shows the change in publications and citations over time for the top ten authors. Table 3 Top ten authors with the highest number of publications Rank Author Freq % of 926 Centrality 1 Laios, Alexandros 11 1.188 0.00 2 Kalampokis, Evangelos 10 1.080 0.00 3 Nugent, David 9 0.971 0.00 4 Thangavelu, Amudha 9 0.971 0.00 5 De jong, Diederick 7 0.756 0.00 6 Fagotti, Anna 6 0.648 0.00 7 Scambia, Giovanni 6 0.648 0.00 8 Theophilou, Georgios 5 0.540 0.00 9 Aghaeepour, Nima 5 0.540 0.00 10 Hutson, Richard 5 0.540 0.00 Fig. 7 A co-occurrence network map of 76 authors who have co-authored two or more publications Fig. 8 Productivity of the ten most productive authors over time Top ten authors with the highest number of publications A co-occurrence network map of 76 authors who have co-authored two or more publications Productivity of the ten most productive authors over time The dual-map overlay function in CiteSpace involves superimposing one map onto another, where the upper map is the overlay map and the lower map is the base map. This method illustrates the relationship between citing and cited domains, providing a visual representation of knowledge diffusion across disciplines at the journal level [ 10 ]. By utilizing the overlay map, the distribution of journals researching AI applications in obstetrics and gynecology within the WoSCC database, as well as the citing journals, can be identified. This approach reveals patterns of knowledge flow related to AI applications in this field at the journal level. Distinct colors differentiate journal sources from various geographic regions, while the thickness of the connecting paths is proportional to their corresponding Z values. As shown in Fig.  9 , the left panel illustrates the primary journal distribution clusters for AI applications in obstetrics and gynecology research within the WoSCC database, while the right panel displays the corresponding cited journal clusters. Curved lines between the two panels indicate citation linkages, clearly depicting citation trajectories. In the left panel, the length of the vertical axis of each ellipse reflects the number of publications in the respective journal, with longer axes indicating higher publication counts, while the length of the horizontal axis corresponds to the number of contributing authors. As detailed in Table  4 , AI research in obstetrics and gynecology is primarily concentrated in the following disciplines: dentistry, dermatology, surgery, medicine, medical, clinical, neurology, sports, ophthalmology, molecular biology, genetics, forensic, anatomy, health, nursing, dermatology, and rehabilitation. Figure  10 displays the distribution of the top ten journals ranked by publication volume, and Table  5 provides detailed bibliometric data for these journals. The journals with the highest publication counts are American Journal of Obstetrics and Gynecology and BMC Pregnancy and Childbirth, each contributing 29 articles ( N  = 29), followed by Scientific Reports with 25 publications ( N  = 25). Among these, American Journal of Obstetrics and Gynecology has the highest impact factor (IF = 8.4). Fig. 9 A double map overlay of AI in obstetrics and gynecology Table 4 Summary of research knowledge flows in artificial intelligence applied to the field of obstetrics and gynecology Citing domain Cited domain Z value Dentistry, dermatology, surgery medicine, medical, clinical neurology, sports, ophthalmology Molecular, biology, genetics forensic, anatomy, medicine 4.2543054 Health, nursing, medicine dermatology, dentistry, surgery sports, rehabilitation, sport 8.940575 Fig. 10 Distribution of top ten journals in terms of publications Table 5 Top ten journals in terms of number of articles published Rank Journal Freq H-index Cite Score (2025) IF (2025) JCI partition 1 American Journal of Obstetrics and Gynecology 29 203 14.20 8.4 Q1 2 BMC Pregnancy and Childbirth 29 66 5.10 2.7 Q1 3 Scientific Reports 25 149 6.70 3.9 Q1 4 European Journal of Obstetrics Gynecology and Reproductive Biology 21 90 3.90 1.9 Q2 5 Gynecologic Oncology 20 147 8.00 4.1 Q1 6 Plos One 20 268 5.40 2.6 Q2 7 Ultrasound in Obstetrics Gynecology 18 128 10.70 6.3 Q1 8 Archives of Gynecology and Obstetrics 17 58 4.30 2.5 Q2 9 Frontiers in Oncology 15 60 6.90 3.3 Q2 10 International Journal of Gynecology obstetrics 15 88 4.70 2.4 Q1 A double map overlay of AI in obstetrics and gynecology Summary of research knowledge flows in artificial intelligence applied to the field of obstetrics and gynecology Dentistry, dermatology, surgery medicine, medical, clinical neurology, sports, ophthalmology Molecular, biology, genetics forensic, anatomy, medicine Health, nursing, medicine dermatology, dentistry, surgery sports, rehabilitation, sport Distribution of top ten journals in terms of publications Top ten journals in terms of number of articles published Keywords serve as concise summaries of a document’s content, offering an efficient means of identifying emerging research hotspots and developmental trends within academic fields [ 11 ]. Analyzing the co-occurrence, clustering, and emergence patterns of keywords provides a deeper understanding of core themes, research hotspots, and frontiers, facilitating the prediction of future research trends and the identification of strategic directions. Following synonym merging using the thesaurus terms.txt file in VOS Viewer, 259 unique keywords were identified. A systematic analysis of keyword frequency was conducted, and a keyword co-occurrence network was constructed using VOS Viewer. Based on the top 20 high-frequency keywords (Table  6 ) and the co-occurrence network visualization (Fig.  11 ), it is clear that assisted diagnosis and treatment, health management, and robotic surgery are current focal points of research. Figure  12 illustrates the keyword co-occurrence map, organized chronologically, revealing the emergence of key terms such as ML, AL, and DL. CiteSpace was used to perform keyword clustering analysis with the log-likelihood ratio (LLR) algorithm, resulting in the clustering map shown in Fig.  13 . The clustering module value ( Q ) was 0.4539, and the average silhouette value ( S ) was 0.7462. Given that Q  > 0.3 and S  > 0.5, the clustering structure is statistically significant, with high internal homogeneity. A total of ten distinct, meaningful clusters were identified: Cluster #0 (ovarian cancer), Cluster #1 (cervical length), Cluster #2 (risk factors), Cluster #3 (placenta previa), Cluster #4 (machine learning), Cluster #5 (artificial intelligence), Cluster #6 (artificial neural networks), Cluster #7 (3D ultrasound), Cluster #8 (length of stay), and Cluster #9 (breast cancer). These clusters exhibit dense interconnections, indicating strong relationships among them. Specifically, clusters #4, #5, and #6 reflect the core AI research directions; clusters #0 and #9 focus on the application of AI in diagnosing and treating gynecological cancers; clusters #1 and #3 address AI applications in obstetrics; clusters #2 and #7 pertain to AI-assisted diagnostic approaches; and cluster #8 relates to hospital length of stay research. Constructing a keyword Timeline graph using CiteSpace enables the visualization of the evolving research hotspots and thematic trends over time, as demonstrated in Fig.  14 . This timeline visualization effectively illustrates the historical development of the research field and clarifies the temporal relationships among various research directions. The application of AI in obstetrics and gynecology began in 1999, initially grounded in technologies such as support vector machines and ML. Over time, research has expanded into areas, such as AI-assisted diagnosis and treatment, malignant tumor analysis, and obstetric applications. Notably, research focused on the diagnosis and treatment of malignant tumors has sustained academic interest into the present day. The temporal evolution of research hotspots in AI applications within obstetrics and gynecology was analyzed using the Bibliometrix R-package, as shown in Fig.  15 . Since 1999, 28 prominent research topics have emerged in this domain. Particularly, the following topics have garnered significant scholarly attention in the past five years: health, AL, HE-4, women, outcomes, management, risk, labor, delivery, pregnancy, classification, birth, term, section, and trial. Table 6 Top 20 keywords in terms of frequency Rank Keyword Freq Total link strength 1 Machine learning 184 817 2 Risk 108 603 3 Women 105 565 4 Artificial intelligence 103 396 5 Labor 89 493 6 Delivery 86 432 7 Pregnancy 85 421 8 Prediction 82 419 9 Ovarian cancer 70 373 10 Diagnosis 69 307 11 Deep learning 66 193 12 Outcomes 63 351 13 Predictive model 61 300 14 Classification 56 278 15 Survival 54 339 16 Management 50 242 17 Nomogram 50 250 18 Risk factors 47 233 19 Surgery 47 236 20 Validation 42 230 Fig. 11 A visual representation in VOS viewer of 259 keywords that appeared more than ten times Fig. 12 A chronological visual map of the keywords Fig. 13 Keyword clustering mapping Fig. 14 A timeline graph from CiteSpace depicting the top ten keyword clusters Fig. 15 A bibliometric examination of “Trending Themes” Top 20 keywords in terms of frequency A visual representation in VOS viewer of 259 keywords that appeared more than ten times A chronological visual map of the keywords Keyword clustering mapping A timeline graph from CiteSpace depicting the top ten keyword clusters A bibliometric examination of “Trending Themes”

The Web of Science Core Collection (WoSCC) undergoes a rigorous evaluation process, ensuring the inclusion of scholarly content that is both credible and influential. Therefore, it serves as an ideal data source for the objectives of this study [ 9 ]. Using WoSCC as the primary data source, the search strategy employed was: [TS = (Obstetric Surgical Procedures OR Gynecologic Surgical Procedures OR Cesarean Section OR Gynecology Surgery OR Cesarean Section OR Episiotomy OR Vaginal Birth After Cesarean OR Hysterotomy OR Labor Delivery OR Vaginal Birth OR Tail of Labor OR Adnexal Surgery OR Ovarian Surgery OR Transvaginal Natural Cavity Endoscope OR Transvaginal Single Port Laparoscopy OR Transvaginal Natural Orifice Transluminal Endoscopic Surgery OR Salpingectomy OR Hysterosoma OR Ovarian Surgery OR Vaginal Birth OR Abortion) AND TS = ("Artificial Intelligence" OR "Machine Learning" OR "Computer Vision" OR "Neural Network*" OR "Deep Learning" OR "Predictive Model" OR "Computational Intelligence" OR "Knowledge Representation" OR "Expert Systems" OR "Computer-Assisted Diagnosis" OR "Image Processing, Computer-Assisted")]. The search covered the period from January 1, 1999, to June 24, 2025, with "English" selected as the language, and the literature types restricted to "articles" and "review articles." Irrelevant publications, such as news articles, conference calls for papers, announcements, and books, were excluded. Two researchers independently conducted the retrieval and screening of the data. In cases of disagreement, a third researcher was consulted for resolution. As a result, 926 articles were included in the final dataset, comprising 865 research articles and 63 review articles. The detailed flowchart outlining this process is presented in Fig.  1 . To ensure data consistency and to circumvent the effects of database updates, the bibliometric data were downloaded in full on June 24, 2025. Two researchers independently retrieved and screened the original data. In case of disagreement, a third researcher made the final decision. The data were downloaded twice for verification. Only after the two researchers confirmed that there were no errors did the study begin. Fig. 1 Detailed literature screening process Detailed literature screening process The full records and cited references of the selected literature were exported in Plain Text File format. Bibliometric analysis and data visualizations were conducted using Excel 2021, VOS Viewer (version 1.6.18), CiteSpace (version 6.2.R6), and the Bibliometrix R-package (version 4.0.1). Excel 2021 was employed to create descriptive charts reflecting the general characteristics of the literature. VOS Viewer, CiteSpace, and the Bibliometrix R-package were used to perform visualization analyses, including patterns of international collaboration, institutional cooperation networks, journal publication trends, keyword co-occurrence, keyword clustering, and emerging research hotspots.

AI research in obstetrics and gynecology holds significant academic value and promising clinical application prospects. In recent years, the volume of scholarly publications has steadily increased, reflecting growing global interest in AI applications within this medical domain. Notably, China and the United States have emerged as leading contributors. Nonetheless, opportunities remain for enhanced international collaboration and institutional exchange. Currently, AI-assisted diagnostic and therapeutic systems are primarily focused on gynecological malignancies, while their application in managing benign gynecological conditions and obstetric disorders remains limited. Furthermore, studies on AI-driven health management solutions are currently underrepresented, highlighting a critical area with high potential for future research and development. Despite technological advancements, the widespread adoption of robotic systems in obstetric and gynecological surgery is still hindered by high operational costs and large physical footprints. Future developmental efforts are expected to prioritize cost reduction, miniaturization of surgical instruments, and the design of portable, mobile cart-based platforms, improving accessibility and usability across diverse clinical settings.

Annual publication volume fluctuations serve as a significant indicator of the development pace and scholarly interest within a field, making it a key metric for assessing the academic landscape. From 1999 to 2024, the annual publication volume of AI applications in obstetrics and gynecology has shown a consistent upward trend. Over the past five years (2020–2024), the average annual publication volume exceeded 100, reflecting increased scholarly attention and indicating a promising future for AI applications in this field. China, the United States, the United Kingdom, Canada, and South Korea are the leading countries in scientific research output on AI applications in obstetrics and gynecology, with established collaborative networks among them. Universities and hospitals are the primary institutions contributing to this domain. Among the top ten most productive institutions by publication volume, four are based in the United States, while two each are from China and Italy, signaling a strong commitment to advancing research in this area by institutions in these countries. The journal distribution pattern reveals that a significant body of research on AI applications in obstetrics and gynecology has been published across journals in obstetrics and gynecology, oncology, reproductive medicine, multidisciplinary sciences, and medical imaging. The American Journal of Obstetrics and Gynecology stands out as the leading publication in terms of article volume, impact factor, and CiteScore within this research domain. An analysis of publication output and collaboration patterns reveals the absence of dominant core authors in this research field, with inter-team collaboration still underdeveloped. Among the contributors, Laios, Alexandros stands out as the most prolific author. His recent work primarily explores the application of natural language processing (NLP) to predict intraoperative and postoperative outcomes in advanced-stage epithelial ovarian cancer cytoreduction (aEOC) [ 12 ], AI-driven predictions of postoperative hospitalization duration and survival rates in patients with high-grade serous ovarian cancer (HGSOC) [ 13 ], and AI-based predictions of factors affecting surgical complexity in advanced epithelial ovarian cancer (EOC) [ 14 ]. Analysis of high-frequency keywords, co-occurrence patterns, and clustering indicates that the primary areas of focus in current research are AI-assisted diagnosis and treatment, AI-driven health management, and robotic surgery in obstetrics and gynecology. The future trajectory of research is expected to remain centered on AI-driven diagnostic and therapeutic approaches. In the early stages of medical information system development, AI was primarily applied to electronic health records. However, with the advent of the big data era, the availability of large-scale datasets has enabled the integration of ML and DL techniques in healthcare. AI’s ability to rapidly and accurately recognize and analyze images offers significant advantages in the screening and diagnosis of gynecological tumors and prenatal assessments. Notably, the interpretation of cervical cytology results remains highly subjective, with low inter-pathologist consistency [ 15 ]. AI has demonstrated a 90% accuracy rate in cervical cell pathological analysis, performing on par with experienced pathologists [ 16 ]. AI algorithms can enhance lesion segmentation accuracy in cervical cancer colposcopy images up to 96% [ 17 ]. Furthermore, AI-assisted systems improve pathologists ’ diagnostic accuracy for serous tubal intraepithelial carcinoma (STIC) by 10% and reduce image interpretation time by one-third [ 18 ]. The integration of AI technology effectively addresses the challenges posed by the critical shortage and varied expertise levels among pathologists. Tanos et al. [ 19 ] developed a Gynecological AL Diagnostics (GAID) model, which achieved an 87% accuracy rate in diagnosing gynecological diseases, covering sub-specialties such as abnormal uterine bleeding, gynecologic endocrinology, gynecologic oncology, pelvic pain disorders, urogynecology, sexually transmitted infections, and vulvoscopic conditions. In prenatal screening, AI systems have significantly improved prediction accuracy for fetal genetic disorders. AI-enhanced ultrasound screening has a 96% detection rate for Down syndrome, though still Lower than the 99% accuracy of non-invasive prenatal DNA testing [ 20 ]. AI’s ability to process extensive clinical datasets, integrate information from various sources, and identify hidden patterns associated with disease risks enhances the accuracy of risk prediction for disease onset. As AI technology advances, models can refine their performance through self-optimization, improving their predictive capabilities. A predictive model incorporating age and 51 laboratory biomarkers has shown high accuracy in forecasting ovarian cancer risk, achieving an area under the receiver-operating characteristic curve (AUC) of 0.95 in an internal validation cohort and AUCs of 0.882 and 0.884 in two external validation cohorts [ 21 ]. Additionally, AI achieves 85% accuracy in predicting preterm birth and 88% in forecasting gestational hypertension, thus improving the precision of individualized risk assessments [ 22 ]. AI also aids in predicting chemotherapy responses, identifying patients most likely to benefit from treatment. Elfiky et al. [ 23 ] conducted a retrospective cohort study of 26,946 oncology patients, employing ML to develop a predictive model for estimating short-term mortality risk (30-day and 180-day) at the initiation of chemotherapy. For patients receiving systemic chemotherapy, predicting 30-day mortality risk could minimize treatment-related adverse outcomes and improve patient quality of life [ 24 ]. Desbois et al. [ 25 ] developed ML methodologies that integrate transcriptomic data and digital pathology for molecular classification and immune phenotype characterization in ovarian cancer. Their findings suggest that transforming growth factor-beta (TGF-β) plays a key role in T-cell exclusion, proposing that targeting TGF-β may enhance the efficacy of immunotherapy in ovarian cancer. Health management refers to a systematic process that involves comprehensive monitoring, assessment, and intervention aimed at improving health outcomes for individuals. Traditional health management models primarily rely on clinical expertise and human resources, focusing on diagnosing and treating existing medical conditions. However, with the rapid advancement of AI technologies, particularly large language models (LLMs), AI has significantly transformed the field. Its impact now spans various aspects of health management, including disease prediction, personalized health planning, continuous health monitoring, chronic disease management, and optimization of epidemiological research and healthcare policies [ 26 ]. The integration of AI addresses key limitations of traditional health management by improving operational efficiency, enabling personalized interventions, optimizing data utilization, and enhancing predictive capabilities [ 27 ]. In obstetrics and gynecology, women’s health needs extend across the entire lifespan, including health management from infancy through adolescence, reproductive and fertility care during the childbearing years, preventive strategies during perimenopause, and targeted interventions in older age. AI has the potential to significantly enhance this holistic approach to women’s health. By utilizing AI-powered big data analytics and personalized assessment tools, gynecologists can more accurately monitor patient health, proactively identify emerging risks, and implement effective, life-cycle-oriented health management strategies. AI-driven applications can monitor menstrual cycles and identify potential health issues, such as polycystic ovary syndrome (PCOS), by analyzing symptoms and cycle irregularities. Automated alerts can prompt users to seek medical consultation when clinically necessary. Women of reproductive age can use wearable devices and digital health platforms to track physiological parameters, such as basal body temperature, heart rate, and sleep patterns, facilitating the prediction of menstrual cycles, ovulation windows, and fertility status [ 28 – 31 ]. These technologies support women aiming to conceive by enabling data-driven family planning and improving preconception care. Additionally, wearable flexible nano-biosensors enable non-invasive, real-time monitoring of the sex hormone estradiol through sweat analysis, offering a promising method for continuous physiological tracking [ 32 ]. AI applications not only empower women to take a proactive role in self-management of their health but also enhance clinical decision-making. By leveraging personalized, data-driven methodologies, AI improves treatment efficacy and facilitates comprehensive health management, from preventive care to targeted interventions. In 1988, Mettler et al. [ 33 ] performed the first gynecological procedure using the AESOP robotic system. In 2005, the U.S. Food and Drug Administration (FDA) approved the Da Vinci robotic surgical platform for gynecologic oncology procedures. A year later, Elliott et al. [ 34 ] reported the first series of 30 patients who underwent robot-assisted sacrocolpopexy, with follow-up assessments performed 2 Years postoperatively, demonstrating favorable outcomes. In 2007, Bocca et al. [ 35 ] documented the world's first robotic-assisted conservative myomectomy, followed by successful conception and full-term delivery. Accumulated evidence suggests that Da Vinci robot-assisted total hysterectomy offers significantly improved perioperative outcomes and enhanced postoperative recovery, particularly for patients with elevated body mass index (BMI) or larger uterine sizes compared to the conventional laparoscopic techniques [ 36 – 39 ]. Currently, the Da Vinci robotic surgical system is the most widely adopted platform in obstetrics and gynecology, with applications spanning both benign and malignant gynecological conditions. Common benign indications include uterine fibroids, abnormal uterine bleeding, endometriosis, and pelvic organ prolapse, while malignant applications focus on the management of endometrial, cervical, and ovarian cancers. The robotic surgical system provides several technical advantages, such as a high-definition three-dimensional magnified operative view, wristed instrumentation with enhanced dexterity, and tremor filtration. These features allow for precise and stable surgical manipulation within confined anatomical spaces, enabling surgeons to more accurately identify and dissect vascular structures and delicate tissue planes during complex procedures [ 40 ]. Robotic surgical systems have established indications for managing benign gynecological conditions, including hysterectomy [ 41 ], myomectomy [ 42 ], sacrocolpopexy [ 43 ], tubal anastomosis [ 44 ], endometriosis-related interventions [ 45 ], cervical cerclage [ 46 ], and robotic-assisted laparo-endoscopic single-site surgery (R-LESS) [ 47 ]. However, the clinical value of robotic surgery in managing benign gynecological conditions remains debated. Some researchers argue that robotic procedures are associated with higher costs and prolonged operative times compared to the conventional surgical methods [ 48 ]. Currently, robotic surgical systems are most commonly used in the treatment of malignant neoplasms, such as cervical, endometrial, and ovarian cancers. Since the initial report of robot-assisted radical hysterectomy in 2005, the use of robotic platforms for cervical cancer management has expanded globally. Evidence suggests that, compared to open surgery, minimally invasive techniques like laparoscopy or robotic-assisted surgery result in reduced intraoperative blood loss, lower complication rates, shorter hospital stays, and comparable oncologic outcomes, with no significant differences in recurrence rates or mortality between robot-assisted radical hysterectomy and traditional open procedures [ 49 – 52 ]. Although robotic applications in obstetrics are less prevalent than in gynecology, several significant implementations have been reported. Sayols et al. [ 53 ] introduced a robotic system designed to assist surgeons in performing remote operations during anastomosis positioning, coagulation, and placental surface examination. The research team also developed and validated the functional performance of this robot-assisted platform. The system features image stabilization and precise localization of anatomical regions, enhancing intraoperative photocoagulation accuracy, facilitating accurate surgical navigation, and ultimately reducing operative duration. Ahmad et al. [ 54 ] developed a compact robotic system aimed at assisting surgeons in performing procedures with enhanced speed and efficiency. Currently, these robotic systems remain in the experimental phase and have not yet been implemented in clinical settings. Additionally, existing physical simulation platforms used in obstetric training lack the ability to replicate dynamic cervical compliance, thus failing to accurately simulate the physiological process of cervical ripening. Luk et al. [ 55 ] presented a novel robotic system capable of simulating cervical ripening during the latent phase of labor. This soft robotic device shows promise as a high-fidelity training simulator for obstetric delivery scenarios. Robot-assisted surgery has evolved into a rapidly advancing field with substantial growth potential. Evidence from current studies indicates that this technology is not only feasible but also capable of delivering effective therapeutic outcomes for carefully selected patient populations [ 56 , 57 ]. The future progression of robotic systems is likely to be driven by advancements in both hardware and software. Emerging trends suggest a shift toward miniaturized surgical instruments, portable cart platforms, and the integration of real-time tissue feedback with radiographic imaging and AI across a variety of clinical applications.

This study has several notable strengths. First, it marks the first application of bibliometric methodologies to systematically analyze AI research within obstetrics and gynecology, offering comprehensive insights and valuable directional guidance for scholars in this field. Second, the study employed three well-established bibliometric tools—VOS Viewer and CiteSpace, both widely recognized and extensively utilized in bibliometric analyses—enhancing the objectivity of the data interpretation process. Third, bibliometric analysis provides a more systematic and nuanced understanding of emerging research hotspots and frontier topics compared to conventional narrative reviews. However, certain limitations must be acknowledged. 1. The dataset was exclusively sourced from the WOSCC, potentially overlooking relevant studies indexed in other databases. 2. The inclusion of only English-language publications may result in the underrepresentation of non-English scholarly contributions. 3. More importantly, publications from 2025 were not incorporated due to data availability constraints. Future research should address these limitations by expanding database coverage and including multilingual and more recent literature, facilitating a more comprehensive and globally representative analysis. 4. The data for this study was sourced from the WOSCC database. Given that this database has been continuously expanding in recent years, it may have a certain impact on the results when analyzing the annual publication trends.

The application of artificial intelligence (AI) in medicine dates back to the mid-twentieth century. In 1952, Howard introduced the concept of the "robot anaesthetist" in the journal Med World, marking one of the earliest explorations into the potential role of machines in medical practice [ 1 , 2 ]. Three years later, John McCarthy formally coined the term "AI," defining it as "the science and engineering of making intelligent machines." With the progression of machine learning (ML) and deep learning (DL) technologies, AI has evolved from an experimental concept to a widely used tool. ML allows AI systems to continuously enhance their performance by learning from experience, while DL leverages multi-layer neural networks to optimize image and data processing efficiency. These advancements have significantly transformed AI’s application in medicine [ 3 ]. The rapid growth of computer science has accelerated AI’s development, leading to its integration across multiple medical specialties. AI has shown immense potential and transformative impact in areas, such as disease screening, medical data analysis, diagnostics, drug discovery, treatment planning, real-time patient monitoring, and medical imaging interpretation, positioning it as a critical technology for enhancing healthcare quality and efficiency. In the field of obstetrics and gynecology, the exploration of AI applications has also been progressively increasing [ 4 ]. Despite the growing interest in AI’s medical applications, bibliometric analyses focusing specifically on AI within obstetrics and gynecology remain scarce. Bibliometrics, a methodological approach that combines both qualitative and quantitative analyses of academic publications, provides valuable insights into the knowledge structure, research hotspots, and developmental trends within specific fields. It has become a key tool for literature-based analysis in medical research [ 5 , 6 ]. Tools like VOS Viewer, CiteSpace, and the Bibliometrix R-package offer powerful visualization and data analysis capabilities, and have been extensively utilized in bibliometric studies [ 7 , 8 ]. This study employed bibliometric techniques to conduct a visualization analysis of the scholarly literature on AI applications in obstetrics and gynecology. Additionally, research trends, emerging hotspots, and potential future directions were systematically identified, providing valuable insights and reference points for future research in this area.