Faculty Bibliography

The rapid evolution of large-language models such as ChatGPT, Claude, and Gemini is reshaping the methodological landscape of obstetrics and gynecology (OBGYN) research. This narrative review provides a comprehensive account of generative AI capabilities, key use-cases, and recommended safeguards for investigators. First, generative AI expedites hypothesis generation, enabling researchers to interrogate vast corpora and surface plausible, overlooked questions. Second, it streamlines systematic reviews by composing optimized search strings, screening titles and abstracts, and identifying full-text discrepancies. Third, AI assistants can draft reproducible analytic code, perform preliminary descriptive or inferential analyses, and create publication-ready tables and figures. Fourth, the models support scholarly writing by suggesting journal-specific headings, refining prose, harmonizing references, and translating technical content for multidisciplinary audiences. Fifth, they augment peer-review and editorial workflows by delivering evidence-focused critiques. In educational settings, these models can create adaptive curricula and interactive simulations for trainees, fostering digital literacy and evidence-based practice early in professional development among clinicians. Integration into clinical decision-support pipelines is also foreseeable, warranting proactive governance. Notwithstanding these opportunities, responsible use demands vigilant oversight. Large-language models occasionally fabricate citations or misinterpret domain-specific data ("hallucinations"), potentially propagating misinformation. Outputs are highly prompt-dependent, creating a reliance on informed prompt engineering that may disadvantage less technical clinicians. Moreover, uploading protected health information or copyrighted text raises privacy, security, and intellectual-property concerns. We outline best-practice recommendations: maintain human verification of all AI-generated content; cross-validate references with primary databases; employ privacy-preserving, on-premises deployments for sensitive data; document prompts for reproducibility; and disclose AI involvement transparently. In summary, generative AI offers a powerful adjunct for OBGYN scientists by accelerating topic formulation, evidence synthesis, data analysis, manuscript preparation, and peer review. When coupled with rigorous oversight and ethical safeguards, these tools can enhance productivity without compromising scientific integrity. Future studies should quantify accuracy, bias, and downstream patient impact.

OBJECTIVE:Social determinants of health (SDOH) may impact the incidence of Respiratory Syncytial Virus (RSV) infection and the uptake of vaccinations in pregnancy. The objective of this study is to identify contributors to disparities in RSV vaccination in pregnancy. DESIGN/METHODS:This is a retrospective cohort study of patients delivering at term within three hospitals during February and March 2024, comparing pregnant patients identified as receiving vs not receiving RSV vaccinations. This period and gestational age were chosen to include patients who would have qualified for RSV vaccination administration. Vaccination status was extracted from standardized admission templates where these variables were recorded as discrete fields. Patients without RSV vaccination information were excluded. Sociodemographic factors, COVID vaccination status, and delivery campus were evaluated. Outcomes were analyzed using chi-squared, t-test, and McNemar test. RESULT/RESULTS:2181 patients met inclusion criteria and RSV vaccination information was available for 1548 patients (71%) with a 14% vaccination rate. Compared to those not vaccinated (n=1332), RSV vaccinated patients (n=216) were more likely to be older (30.7 vs 34.8, p<0.001), have private insurance (42% vs 85%, p<0.001), speak English (82% vs 95%, p<0.001), and deliver at our regional perinatal center (26% vs 77%, p<0.001). 50% of RSV vaccinated patients had a history of COVID vaccination compared to 33% of those not vaccinated against RSV (p<0.001). CONCLUSIONS:SDOH were associated with differences in RSV vaccination status. In addition, patients without RSV vaccination were less likely to have had COVID vaccination. These findings highlight the need to address SDOH to increase vaccination rates for vulnerable populations.

BACKGROUND:Diabetes mellitus is a common medical complication of pregnancy, and its treatment is complex. Recent years have seen an increase in the application of mobile health tools and advanced technologies, such as remote patient monitoring, with the aim of improving care for diabetes mellitus in pregnancy. Previous studies of these technologies for the treatment of diabetes in pregnancy have been small and have not clearly shown clinical benefit with implementation. OBJECTIVE:Remote patient monitoring allows clinicians to monitor patients' health data (such as glucose values) in near real-time, between office visits, to make timely adjustments to care. Our objective was to determine if using remote patient monitoring for the management of diabetes in pregnancy leads to an improvement in maternal and neonatal outcomes. STUDY DESIGN/METHODS:This was a retrospective cohort study of pregnant patients with diabetes mellitus managed by the maternal-fetal medicine practice at one academic institution between October 2019 and April 2021. This practice transitioned from paper-based blood glucose logs to remote patient monitoring in February 2020. Remote patient monitoring options included (1) device integration with Bluetooth glucometers that automatically uploaded measured glucose values to the patient's Epic MyChart application or (2) manual entry in which patients manually logged their glucose readings into their MyChart application. Values in the MyChart application directly transferred to the patient's electronic health record for review and management by clinicians. In total, 533 patients were studied. We compared 173 patients managed with paper logs to 360 patients managed with remote patient monitoring (176 device integration and 184 manual entry). Our primary outcomes were composite maternal morbidity (which included third- and fourth-degree lacerations, chorioamnionitis, postpartum hemorrhage requiring transfusion, postpartum hysterectomy, wound infection or separation, venous thromboembolism, and maternal admission to the intensive care unit) and composite neonatal morbidity (which included umbilical cord pH <7.00, 5 minute Apgar score <7, respiratory morbidity, hyperbilirubinemia, meconium aspiration, intraventricular hemorrhage, necrotizing enterocolitis, sepsis, pneumonia, seizures, hypoxic ischemic encephalopathy, shoulder dystocia, trauma, brain or body cooling, and neonatal intensive care unit admission). Secondary outcomes were measures of glycemic control and the individual components of the primary composite outcomes. We also performed a secondary analysis in which the patients who used the two different remote patient monitoring options (device integration vs manual entry) were compared. Chi-square, Fisher's exact, 2-sample t, and Mann-Whitney tests were used to compare the groups. A result was considered statistically significant at P<.05. RESULTS:Maternal baseline characteristics were not significantly different between the remote patient monitoring and paper groups aside from a slightly higher baseline rate of chronic hypertension in the remote patient monitoring group (6.1% vs 1.2%; P=.011). The primary outcomes of composite maternal and composite neonatal morbidity were not significantly different between the groups. However, remote patient monitoring patients submitted more glucose values (177 vs 146; P=.008), were more likely to achieve glycemic control in target range (79.2% vs 52.0%; P<.0001), and achieved the target range sooner (median, 3.3 vs 4.1 weeks; P=.025) than patients managed with paper logs. This was achieved without increasing in-person visits. Remote patient monitoring patients had lower rates of preeclampsia (5.8% vs 15.0%; P=.0006) and their infants had lower rates of neonatal hypoglycemia in the first 24 hours of life (29.8% vs 51.7%; P<.0001). CONCLUSION/CONCLUSIONS:Remote patient monitoring for the management of diabetes mellitus in pregnancy is superior to a traditional paper-based approach in achieving glycemic control and is associated with improved maternal and neonatal outcomes.

With the advent of artificial intelligence that not only can learn from us but also can communicate with us in plain language, humans are embarking on a brave new future. The interaction between humans and artificial intelligence has never been so widespread. Chat Generative Pre-trained Transformer is an artificial intelligence resource that has potential uses in the practice of medicine. As clinicians, we have the opportunity to help guide and develop new ways to use this powerful tool. Optimal use of any tool requires a certain level of comfort. This is best achieved by appreciating its power and limitations. Being part of the process is crucial in maximizing its use in our field. This clinical opinion demonstrates the potential uses of Chat Generative Pre-trained Transformer for obstetrician-gynecologists and encourages readers to serve as the driving force behind this resource.