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Abstract Background Data sciences and artificial intelligence are becoming encouraging tools in assisted reproduction, favored by time-lapse technology incubators. Our objective is to analyze, compare and identify the most predictive machine learning algorithm developed using a known implantation database of embryos transferred in our egg donation program, including morphokinetic and morphological variables, and recognize the most predictive embryo parameters in order to enhance IVF treatments clinical outcomes. Methods Multicenter retrospective cohort study carried out in 378 egg donor recipients who performed a fresh single embryo transfer during 2021. All treatments were performed by Intracytoplasmic Sperm Injection, using fresh or frozen oocytes. The embryos were cultured in Geri® time-lapse incubators until transfer on day 5. The embryonic morphokinetic events of 378 blastocysts with known implantation and live birth were analyzed. Classical statistical analysis (binary logistic regression) and 10 machine learning algorithms were applied including Multi-Layer Perceptron, Support Vector Machines, k-Nearest Neighbor, Cart and C0.5 Classification Trees, Random Forest (RF), AdaBoost Classification Trees, Stochastic Gradient boost, Bagged CART and eXtrem Gradient Boosting. These algorithms were developed and optimized by maximizing the area under the curve. Results The Random Forest emerged as the most predictive algorithm for implantation (area under the curve, AUC = 0.725, IC 95% [0.6232–0826]). Overall, implantation and miscarriage rates stood at 56.08% and 18.39%, respectively. Overall live birth rate was 41.26%. Significant disparities were observed regarding time to hatching out of the zona pellucida (p = 0.039). The Random Forest algorithm demonstrated good predictive capabilities for live birth (AUC = 0.689, IC 95% [0.5821–0.7921]), but the AdaBoost classification trees proved to be the most predictive model for live birth (AUC = 0.749, IC 95% [0.6522–0.8452]). Other important variables with substantial predictive weight for implantation and live birth were duration of visible pronuclei (DESAPPN-APPN), synchronization of cleavage patterns (T8-T5), duration of compaction (TM-TiCOM), duration of compaction until first sign of cavitation (TiCAV-TM) and time to early compaction (TiCOM). Conclusions This study highlights Random Forest and AdaBoost as the most effective machine learning models in our Known Implantation and Live Birth Database from our egg donation program. Notably, time to blastocyst hatching out of the zona pellucida emerged as a highly reliable parameter significantly influencing our implantation machine learning predictive models. Processes involving syngamy, genomic imprinting during embryo cleavage, and embryo compaction are also influential and could be crucial for implantation and live birth outcomes.

Abstract Background Cervical cancer (CC) and breast cancer (BC) threaten women’s well-being, influenced by health-related stigma and a lack of reliable information, which can cause late diagnosis and early death. ChatGPT is likely to become a key source of health information, although quality concerns could also influence health-seeking behaviours. Methods This cross-sectional online survey compared ChatGPT’s responses to five physicians specializing in mammography and five specializing in gynaecology. Twenty frequently asked questions about CC and BC were asked on 26th and 29th of April, 2023. A panel of seven experts assessed the accuracy, consistency, and relevance of ChatGPT’s responses using a 7-point Likert scale. Responses were analyzed for readability, reliability, and efficiency. ChatGPT’s responses were synthesized, and findings are presented as a radar chart. Results ChatGPT had an accuracy score of 7.0 (range: 6.6-7.0) for CC and BC questions, surpassing the highest-scoring physicians (P

Abstract Background The occurrence of blastocyst collapse may become an indicator of preimplantation embryo quality assessment. It has been reported that collapsing blastocysts can lead to higher rates of aneuploidy and poorer clinical outcomes, but more large-scale studies are needed to explore this relationship. This study explored the characteristics of blastocyst collapse identified and quantified by artificial intelligence and explored the associations between blastocyst collapse and embryo ploidy, morphological quality, and clinical outcomes. Methods This observational study included data from 3288 biopsied blastocysts in 1071 time-lapse preimplantation genetic testing cycles performed between January 2019 and February 2023 at a single academic fertility center. All transferred blastocysts are euploid blastocysts. The artificial intelligence recognized blastocyst collapse in time-lapse microscopy videos and then registered the collapsing times, and the start time, the recovery duration, the shrinkage percentage of each collapse. The effects of blastocyst collapse and embryo ploidy, pregnancy, live birth, miscarriage, and embryo quality were studied using available data from 1196 euploid embryos and 1300 aneuploid embryos. Results 5.6% of blastocysts collapsed at least once only before the full blastocyst formation (tB), 19.4% collapsed at least once only after tB, and 3.1% collapsed both before and after tB. Multiple collapses of blastocysts after tB (times ≥ 2) are associated with higher aneuploid rates (54.6%, P > 0.05; 70.5%, P

Objective: The global population is aging and the burden of lower urinary tract symptoms (LUTS) is expected to increase. According to the National Health Insurance Research Database, our previous studies have showed LUTS may predispose patients to cardiovascular disease. However, it is difficult to provide a personalized risk assessment in the context of “having acute coronary syndrome (ACS) and stroke.” This study aimed to develop an artificial intelligence (AI)-based prediction model for patients with LUTS. Material and methods: We retrospectively reviewed the electronic medical records of 1799 patients with LUTS at Chi Mei Medical Center between January 1, 2001 and December, 31, 2018. Features with >10 cases and high correlations with outcomes were imported into six machine learning algorithms. The study outcomes included ACS and stroke. Model performances was evaluated using the area under the receiver operating characteristic curve (AUC). The model with the highest AUC was used to implement the clinical risk prediction application. Results: Age, systemic blood pressure, diastolic blood pressure, creatinine, glycated hemoglobin, hypertension, diabetes mellitus and hyperlipidemia were the most relevant features that affect the outcomes. Based on the AUC, our optimal model was built using multilayer perception (AUC = 0.803) to predict ACS and stroke events within 3 years. Conclusion: We successfully built an AI-based prediction system that can be used as a prediction model to achieve time-saving, precise, personalized risk evaluation; it can also be used to offer warning, enhance patient adherence, early intervention and better health care outcomes.

Abstract Background Cervical cancer is the fourth most frequent cancer among women, with 90% of cervical cancer-related deaths occurring in low- and middle-income countries like Cameroon. Visual inspection with acetic acid is often used in low-resource settings to screen for cervical cancer; however, its accuracy can be limited. To address this issue, the Swiss Federal Institute of Technology Lausanne and the University Hospitals of Geneva are collaborating to develop an automated smartphone-based image classifier that serves as a computer aided diagnosis tool for cancerous lesions. The primary objective of this study is to explore the acceptability and perspectives of women in Dschang regarding the usage of a screening tool for cervical cancer relying on artificial intelligence. A secondary objective is to understand the preferred form and type of information women would like to receive regarding this artificial intelligence-based screening tool. Methods A qualitative methodology was employed to gain better insight into the women’s perspectives. Participants, aged between 30 and 49 were invited from both rural and urban regions and semi-structured interviews using a pre-tested interview guide were conducted. The focus groups were divided on the basis of level of education, as well as HPV status. The interviews were audio-recorded, transcribed, and coded using the ATLAS.ti software. Results A total of 32 participants took part in the six focus groups, and 38% of participants had a primary level of education. The perspectives identified were classified using an adapted version of the Technology Acceptance Model. Key factors influencing the acceptability of artificial intelligence include privacy concerns, perceived usefulness, and trust in the competence of providers, accuracy of the tool as well as the potential negative impact of smartphones. Conclusion The results suggest that an artificial intelligence-based screening tool for cervical cancer is mostly acceptable to the women in Dschang. By ensuring patient confidentiality and by providing clear explanations, acceptance can be fostered in the community and uptake of cervical cancer screening can be improved. Trial registration Ethical Cantonal Board of Geneva, Switzerland (CCER, N°2017–0110 and CER-amendment n°4) and Cameroonian National Ethics Committee for Human Health Research (N°2022/12/1518/CE/CNERSH/SP). NCT: 03757299.

Abstract Background The best method for selecting embryos ploidy is preimplantation genetic testing for aneuploidies (PGT-A). However, it takes more labour, money, and experience. As such, more approachable, non- invasive techniques were still needed. Analyses driven by artificial intelligence have been presented recently to automate and objectify picture assessments. Methods In present retrospective study, a total of 3448 biopsied blastocysts from 979 Time-lapse (TL)-PGT cycles were retrospectively analyzed. The “intelligent data analysis (iDA) Score” as a deep learning algorithm was used in TL incubators and assigned each blastocyst with a score between 1.0 and 9.9. Results Significant differences were observed in iDAScore among blastocysts with different ploidy. Additionally, multivariate logistic regression analysis showed that higher scores were significantly correlated with euploidy (p

Ishfaq Yaseen,1 Riyaz Ahmad Rather2 1Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia; 2Department of Biotechnology, College of Natural and Computational Science, Wachemo University, Hossana, EthiopiaCorrespondence: Riyaz Ahmad Rather, Department of Biotechnology, College of Natural and Computational Science, Wachemo University, Post Box-667, Hossana, Ethiopia, Tel +251 988937374, Email farhan.rizu@gmail.comAbstract: The implementation of Artificial Intelligence (AI) in healthcare is enhancing diagnostic accuracy in clinical setups. The use of AI in healthcare is steadily increasing with advancing technology, extending beyond disease diagnosis to encompass roles in feto-maternal health. AI harnesses Machine Learning (ML), Natural Language Processing (NLP), Artificial Neural Networks (ANN), and computer vision to analyze data and draw conclusions. Considering maternal health, ML analyzes vast datasets to predict maternal and fetal health outcomes, while NLP interprets medical texts and patient records to assist in diagnosis and treatment decisions. ANN models identify patterns in complex feto-maternal medical data, aiding in risk assessment and intervention planning whereas, computer vision enables the analysis of medical images for early detection of feto-maternal complications. AI facilitates early pregnancy detection, genetic screening, and continuous monitoring of maternal health parameters, providing real-time alerts for deviations, while also playing a crucial role in the early detection of fetal abnormalities through enhanced ultrasound imaging, contributing to informed decision-making. This review investigates into the application of AI, particularly through predictive models, in addressing the monitoring of feto-maternal health. Additionally, it examines potential future directions and challenges associated with these applications.Keywords: Artificial intelligence, feto-maternal health, fetal monitoring, machine learning, Artificial neural networks

Abstract Background Current guidelines regarding oxytocin stimulation are not tailored to individuals as they are based on randomised controlled trials. The objective of the study was to develop an artificial intelligence (AI) model for individual prediction of the risk of caesarean delivery (CD) in women with a cervical dilatation of 6 cm after oxytocin stimulation for induced labour. The model included not only variables known when labour induction was initiated but also variables describing the course of the labour induction. Methods Secondary analysis of data from the CONDISOX randomised controlled trial of discontinued vs. continued oxytocin infusion in the active phase of induced labour. Extreme gradient boosting (XGBoost) software was used to build the prediction model. To explain the impact of the predictors, we calculated Shapley additive explanation (SHAP) values and present a summary SHAP plot. A force plot was used to explain specifics about an individual’s predictors that result in a change of the individual’s risk output value from the population-based risk. Results Among 1060 included women, 160 (15.1%) were delivered by CD. The XGBoost model found women who delivered vaginally were more likely to be parous, taller, to have a lower estimated birth weight, and to be stimulated with a lower amount of oxytocin. In 108 women (10% of 1060) the model favoured either continuation or discontinuation of oxytocin. For the remaining 90% of the women, the model found that continuation or discontinuation of oxytocin stimulation affected the risk difference of CD by less than 5% points. Conclusion In women undergoing labour induction, this AI model based on a secondary analysis of data from the CONDISOX trial may help predict the risk of CD and assist the mother and clinician in individual tailored management of oxytocin stimulation after reaching 6 cm of cervical dilation.

Abstract Background People with polycystic ovary syndrome suffer from many symptoms and are at risk of developing diseases such as hypertension and diabetes in the future. Therefore, the importance of self-care doubles. It is mainly to modify the lifestyle, especially following the principles of healthy eating. The purpose of this study is to review artificial intelligence-based systems for providing management recommendations, especially food recommendations. Materials and methods This study started by searching three databases: PubMed, Scopus, and Web of Science, from inception until 6 June 2023. The result was the retrieval of 15,064 articles. First, we removed duplicate studies. After the title and abstract screening, 119 articles remained. Finally, after reviewing the full text of the articles and considering the inclusion and exclusion criteria, 20 studies were selected for the study. To assess the quality of articles, we used criteria proposed by Malhotra, Wen, and Kitchenham. Out of the total number of included studies, seventeen studies were high quality, while three studies were moderate quality. Results Most studies were conducted in India in 2021. Out of all the studies, diagnostic recommendation systems were the most frequently researched, accounting for 86% of the total. Precision, sensitivity, specificity, and accuracy were more common than other performance metrics. The most significant challenge or limitation encountered in these studies was the small sample size. Conclusion Recommender systems based on artificial intelligence can help in fields such as prediction, diagnosis, and management of polycystic ovary syndrome. Therefore, since there are no nutritional recommendation systems for these patients in Iran, this study can serve as a starting point for such research.

Cosmetic and functional gynecology have gained popularity among patients, but the scientific literature in this field, particularly regarding the cosmetic aspect, is lacking. The use of evidence-based medicine is crucial to validate diagnostic tools and treatment protocols. However, the advent of artificial intelligence (AI) offers a promising solution to address this issue. ChatGPT, a sophisticated language model, can revolutionize AI in medicine, enabling accurate diagnosis, personalized treatment plans, and expedited research analysis. Cosmetic and functional gynecology can leverage AI to develop the field and improve evidence gathering. AI can aid in precise and personalized diagnosis, implement standardized assessment tools, simulate treatment outcomes, and assess under-skin anatomy through virtual reality. AI tools can assist clinicians in diagnosing and comparing difficult cases, calculate treatment risks, and contribute to standardization by collecting global evidence and generating guidelines. The use of AI in cosmetic and functional gynecology holds significant potential to advance the field and improve patient outcomes. This novel combination of AI and gynecology represents a groundbreaking development in medicine, emphasizing the importance of appropriate and correct AI usage.

Abstract Background Artificial Intelligence entails the application of computer algorithms to the huge and heterogeneous amount of morphodynamic data produced by Time-Lapse Technology. In this context, Machine Learning (ML) methods were developed in order to assist embryologists with automatized and objective predictive models able to standardize human embryo assessment. In this study, we aimed at developing a novel ML-based strategy to identify relevant patterns associated with the prediction of blastocyst development stage on day 5. Methods We retrospectively analysed the morphokinetics of 575 embryos obtained from 80 women who underwent IVF at our Unit. Embryo morphokinetics was registered using the Geri plus® time-lapse system. Overall, 30 clinical, morphological and morphokinetic variables related to women and embryos were recorded and combined. Some embryos reached the expanded blastocyst stage on day 5 (BL Group, n = 210), some others did not (nBL Group, n = 365). Results The novel EmbryoMLSelection framework was developed following four-steps: Feature Selection, Rules Extraction, Rules Selection and Rules Evaluation. Six rules composed by a combination of 8 variables were finally selected, and provided a predictive power described by an AUC of 0.84 and an accuracy of 81%. Conclusions We provided herein a new feature-signature able to identify with an high performance embryos with the best developmental competence to reach the expanded blastocyst stage on day 5. Clear and clinically relevant cut-offs were identified for each considered variable, providing an objective tool for early embryo developmental assessment.

The use of chatbot technology, particularly chat generative pre-trained transformer (ChatGPT) with an impressive 175 billion parameters, has garnered significant attention across various domains, including Obstetrics and Gynecology (OBGYN). This comprehensive review delves into the transformative potential of chatbots with a special focus on ChatGPT as a leading artificial intelligence (AI) technology. Moreover, ChatGPT harnesses the power of deep learning algorithms to generate responses that closely mimic human language, opening up myriad applications in medicine, research, and education. In the field of medicine, ChatGPT plays a pivotal role in diagnosis, treatment, and personalized patient education. Notably, the technology has demonstrated remarkable capabilities, surpassing human performance in OBGYN examinations, and delivering highly accurate diagnoses. However, challenges remain, including the need to verify the accuracy of the information and address the ethical considerations and limitations. In the wide scope of chatbot technology, AI systems play a vital role in healthcare processes, including documentation, diagnosis, research, and education. Although promising, the limitations and occasional inaccuracies require validation by healthcare professionals. This review also examined global chatbot adoption in healthcare, emphasizing the need for user awareness to ensure patient safety. Chatbot technology holds great promise in OBGYN and medicine, offering innovative solutions while necessitating responsible integration to ensure patient care and safety.

Abstract Purpose The introduction of the time-lapse monitoring system (TMS) and the development of predictive algorithms could contribute to the optimal embryos selection for transfer. Therefore, the present study aims at investigating the efficiency of KIDScore and iDAScore systems for blastocyst stage embryos in predicting live birth events. Methods The present retrospective study was conducted in a private IVF Unit setting throughout a 10-month period from October 2021 to July 2022, and included the analysis of 429 embryos deriving from 91 IVF/ICSI cycles conducted due to infertility of various etiologies. Embryos incubated at the Embryoscope+ timelapse incubator were analyzed through the established scoring systems: KIDScore and iDAScore®. The main outcome measure was the comparison of the two scoring systems in terms of live birth prediction. Embryos with the higher scores at day 5 (KID5 score/iDA5 score) were transferred or cryopreserved for later use. Results Embryos with high KID5 and iDA5 scores positively correlated with the probability of successful live birth, with KID5 score yielding a higher efficiency in predicting a successful reproductive outcome compared to a proportionally high iDA5 score. KID5 demonstrated conservative performance in successfully predicting live birth compared to iDA5 score, indicating that an efficient prediction can be either provided by a relatively lower KID5 score or a relatively higher iDA5 score. Conclusion The developed artificial intelligence tools should be implemented in clinical practice in conjunction with the conventional morphological assessment for the conduction of optimized embryo transfer in terms of a successful live birth.

Abstract Background This study presents CUPID, an advanced automated measurement software based on Artificial Intelligence (AI), designed to evaluate nine fetal biometric parameters in the mid-trimester. Our primary objective was to assess and compare the CUPID performance of experienced senior and junior radiologists. Materials and methods This prospective cross-sectional study was conducted at Shenzhen University General Hospital between September 2022 and June 2023, and focused on mid-trimester fetuses. All ultrasound images of the six standard planes, that enabled the evaluation of nine biometric measurements, were included to compare the performance of CUPID through subjective and objective assessments. Results There were 642 fetuses with a mean (±SD) age of 22 ± 2.82 weeks at enrollment. In the subjective quality assessment, out of 642 images representing nine biometric measurements, 617-635 images (90.65-96.11%) of CUPID caliper placements were determined to be accurately placed and did not require any adjustments. Whereas, for the junior category, 447-691 images (69.63-92.06%) were determined to be accurately placed and did not require any adjustments. In the objective measurement indicators, across all nine biometric parameters and estimated fetal weight (EFW), the intra-class correlation coefficients (ICC) (0.843-0.990) and Pearson correlation coefficients (PCC) (0.765-0.978) between the senior radiologist and CUPID reflected good reliability compared with the ICC (0.306-0.937) and PCC (0.566-0.947) between the senior and junior radiologists. Additionally, the mean absolute error (MAE), percentage error (PE), and average error in days of gestation were lower between the senior and CUPID compared to the difference between the senior and junior radiologists. The specific differences are as follows: MAE (0.36-2.53 mm, 14.67 g) compared to (0.64- 8.13 mm, 38.05 g), PE (0.94-9.38%) compared to (1.58-16.04%), and average error in days (3.99-7.92 days) compared to (4.35-11.06 days). In the time-consuming task, CUPID only takes 0.05-0.07 s to measure nine biometric parameters, while senior and junior radiologists require 4.79-11.68 s and 4.95-13.44 s, respectively. Conclusions CUPID has proven to be highly accurate and efficient software for automatically measuring fetal biometry, gestational age, and fetal weight, providing a precise and fast tool for assessing fetal growth and development.

Abstract The quandary known as the Intracytoplasmic Sperm Injection (ICSI) paradox is found at the juncture of Assisted Reproductive Technology (ART) and ‘andrological ignorance’ – a term coined to denote the undervalued treatment and comprehension of male infertility. The prevalent use of ICSI as a solution for severe male infertility, despite its potential to propagate genetically defective sperm, consequently posing a threat to progeny health, illuminates this paradox. We posit that the meteoric rise in Industrial Revolution 4.0 (IR 4.0) and Artificial Intelligence (AI) technologies holds the potential for a transformative shift in addressing male infertility, specifically by mitigating the limitations engendered by ‘andrological ignorance.’ We advocate for the urgent need to transcend andrological ignorance, envisaging AI as a cornerstone in the precise diagnosis and treatment of the root causes of male infertility. This approach also incorporates the identification of potential genetic defects in descendants, the establishment of knowledge platforms dedicated to male reproductive health, and the optimization of therapeutic outcomes. Our hypothesis suggests that the assimilation of AI could streamline ICSI implementation, leading to an overall enhancement in the realm of male fertility treatments. However, it is essential to conduct further investigations to substantiate the efficacy of AI applications in a clinical setting. This article emphasizes the significance of harnessing AI technologies to optimize patient outcomes in the fast-paced domain of reproductive medicine, thereby fostering the well-being of upcoming generations.

Abstract Background This systematic review provides an overview of machine learning (ML) approaches for predicting preeclampsia. Method This review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) guidelines. We searched the Cochrane Central Register, PubMed, EMBASE, ProQuest, Scopus, and Google Scholar up to February 2023. Search terms were limited to “preeclampsia” AND “artificial intelligence” OR “machine learning” OR “deep learning.” All studies that used ML-based analysis for predicting preeclampsia in pregnant women were considered. Non-English articles and those that are unrelated to the topic were excluded. The PROBAST was used to assess the risk of bias and applicability of each included study. Results The search strategy yielded 128 citations; after duplicates were removed and title and abstract screening was completed, 18 full-text articles were evaluated for eligibility. Four studies were included in this review. Two studies were at low risk of bias, and two had low to moderate risk. All of the study designs included were retrospective cohort studies. Nine distinct models were chosen as ML models from the four studies. Maternal characteristics, medical history, medication intake, obstetrical history, and laboratory and ultrasound findings obtained during prenatal care visits were candidate predictors to train the ML model. Elastic net, stochastic gradient boosting, extreme gradient boosting, and Random forest were among the best models to predict preeclampsia. All four studies used metrics such as the area under the curve, true positive rate, negative positive rate, accuracy, precision, recall, and F1 score. The AUC of ML models varied from 0.860 to 0.973 in four studies. Conclusion The results of studies yielded high prediction performance of ML models for preeclampsia risk from routine early pregnancy information.

Abstract Background Several studies have demonstrated that iDAScore is more accurate in predicting pregnancy outcomes in cycles without preimplantation genetic testing for aneuploidy (PGT-A) compared to KIDScore and the Gardner criteria. However, the effectiveness of iDAScore in cycles with PGT-A has not been thoroughly investigated. Therefore, this study aims to assess the association between artificial intelligence (AI)-based iDAScore (version 1.0) and pregnancy outcomes in single-embryo transfer (SET) cycles with PGT-A. Methods This retrospective study was approved by the Institutional Review Board of Chung Sun Medical University, Taichung, Taiwan. Patients undergoing SET cycles (n = 482) following PGT-A at a single reproductive center between January 2017 and June 2021. The blastocyst morphology and morphokinetics of all embryos were evaluated using a time-lapse system. The blastocysts were ranked based on the scores generated by iDAScore, which were defined as AI scores, or by KIDScore D5 (version 3.2) following the manufacturer’s protocols. A single blastocyst without aneuploidy was transferred after examining the embryonic ploidy status using a next-generation sequencing-based PGT-A platform. Logistic regression analysis with generalized estimating equations was conducted to assess whether AI scores are associated with the probability of live birth (LB) while considering confounding factors. Results Logistic regression analysis revealed that AI score was significantly associated with LB probability (adjusted odds ratio [OR] = 2.037, 95% confidence interval [CI]: 1.632–2.542) when pulsatility index (PI) level and types of chromosomal abnormalities were controlled. Blastocysts were divided into quartiles in accordance with their AI score (group 1: 3.0–7.8; group 2: 7.9–8.6; group 3: 8.7–8.9; and group 4: 9.0–9.5). Group 1 had a lower LB rate (34.6% vs. 59.8–72.3%) and a higher rate of pregnancy loss (26% vs. 4.7–8.9%) compared with the other groups (p

Purpose: Digitalization plays a critical role and is beginning to impact every part of the patient journey, from drug discovery and data collection to treatment and patient-reported outcomes. We aimed to evaluate the status quo and future directions of digital medicine in the specialty of gynecology and obstetrics in Germany. Methods: An anonymous questionnaire was distributed via the German Society of Gynecology and Obstetrics newsletter in December 2022. The questionnaire covered the domains baseline demographic information, telemedicine, digital health applications (DIGAs), and future expectations. Results: In all, 91 participants completed the survey. Median age was 34 years; 67.4% (60 of 89) were female and 32.6% (29 of 89) were male. About 10% (9 of 88) have prescribed DIGAs to date and 14% (12 of 86) offer telemedical appointments. Among those who do not use digital medicine, very few plan to do so in the near future. Reasons include missing software interfaces, lack of time to try out new things, lack of knowledge, lack of monetary compensation (66.3%), and employee concerns. A majority agreed that digitalization will help to save time and improve patient care and that intelligent algorithms will aid clinicians in providing patient care to women. Conclusions: The status quo and future directions of digital medicine in gynecology and obstetrics in Germany are characterized by contradicting expectations regarding the benefits of digital medicine and its actual implementation in clinical routine. This represents an important call to action to meet the requirements of modern patient care. [ABSTRACT FROM AUTHOR]

Early diagnosis and screening of ovarian cancer remain significant challenges to improving patient outcomes. There is an urgent need to implement both established and modern strategies to address the “early detection” conundrum, especially as new research continues to uncover the complexities of the disease. The discussion provided is the result of a unique research conference focused on reviewing early detection modalities and providing insight into future approaches.