Intelligence artificielle pour la visualisation des changements de couverture terrestre en Chine centrale

Background: The architecture of Software Defined Networking (SDN) integrated with Vehicular Ad-hoc Networks (VANETs) is considered a practical method for handling large-scale, dynamic, heterogeneous vehicular networks, since it offers flexibility, programmability, scalability, and a global understanding. However, the integration with VANETs introduces additional security vulnerabilities due to the deployment of a logically centralized control mechanism. These security attacks are classified as internal and external based on the nature of the attacker. The method adopted in this work facilitated the detection of internal position falsification attacks. Objective: This study aimed to investigate the performance of k-NN, SVM, Naïve Bayes, Logistic Regression, and Random Forest machine learning (ML) algorithms in detecting position falsification attacks using the Vehicular Reference Misbehavior (VeReMi) dataset. It also aimed to conduct a comparative analysis of two ensemble classification models, namely voting and stacking for final decision-making. These ensemble classification methods used the ML algorithms cooperatively to achieve improved classification. Methods: The simulations and evaluations were conducted using the Python programming language. VeReMi dataset was selected since it was an application-specific dataset for VANETs environment. Performance evaluation metrics, such as accuracy, precision, recall, F-measure, and prediction time were also used in the comparative studies. Results: This experimental study showed that Random Forest ML algorithm provided the best performance in detecting attacks among the ML algorithms. Voting and stacking were both used to enhance classification accuracy and reduce time required to identify an attack through predictions generated by k-NN, SVM, Naïve Bayes, Logistic Regression, and Random Forest classifiers. Conclusion: In terms of attack detection accuracy, both methods (voting and stacking) achieved the same level of accuracy as Random Forest. However, the detection of attack using stacking could be achieved in roughly less than half the time required by voting ensemble. Keywords: Machine learning methods, Majority voting ensemble, SDN-based VANETs, Security attacks, Stacking ensemble classifiers, VANETs,

Objective: To develop a machine learning (ML) framework to identify postprandial glucose responses (PPGR) automatically from continuous glucose monitoring (CGM) data in pregnant adults with gestational diabetes mellitus (GDM). Methods: Pregnant adults diagnosed with GDM or impaired glucose tolerance (IGT) wore blinded CGMs and logged mealtimes for up to three 14-day time periods after enrollment. A random forest ML algorithm was applied to identify morning PPGRs from daily CGM profiles, and its performance compared against PPGRs derived using self-reported mealtimes. Results: Twenty-one participants provided analyzable data. Relative to self-reported mealtime, the ML algorithm’s predicted mealtimes had an absolute error of a median 30 (interquartile range [IQR]: 20–45) min. Comparing 1-h and 2-h PPGR values from the CGM using self-reported and ML-predicted mealtimes showed a median difference of 8.7 (IQR: 0–22.7) mg/dL and 3.3 (IQR: 0–13.2) mg/dL, respectively, for the two timepoints. Conclusions: A random forest ML algorithm accurately identified PPGRs from CGM data in persons with GDM, enabling an automated and convenient approach to monitoring postprandial dysglycemia in this population.

Visually Impaired People (VIP) face significant challenges in their daily lives, relying on others or trained dogs for assistance when navigating outdoors. Researchers have developed the Smart Stick (SS) system as a more effective aid than traditional ones to address these challenges. Developing and utilizing the SS systems for VIP improves mobility, reliability, safety, and accessibility. These systems help users by identifying obstacles and hazards, keeping VIP safe and efficient. This paper presents the design and real-world implementation of an SS using an Arduino Nano microcontroller, GPS, GSM module, heart rate sensor, ultrasonic sensor, moisture sensor, vibration motor, and Buzzer. Based on sensor data, the SS can provide warning signals to VIP about the presence of obstacles and hazards around them. Several Machine Learning (ML) algorithms were used to improve the SS alert decision accuracy. Therefore, this paper used sensor data to train and test ten ML algorithms to find the most effective alert decision accuracy. Based on the ML algorithms, the alert decision, including the presence of obstacles, environmental conditions, and user health conditions, was examined using several performance metrics. Results showed that the AdaBoost, Gradient boosting, and Random Forest ML algorithms outperformed others and achieved an AUC and specificity of 100%, with 99.9% accuracy, F1-score, precision, recall, and MCC in the cross-validation phase. Integrating sensor data with ML algorithms revealed that the SS enables VIP to live independently and move safely without assistance.

The detection and identification of Distributed Denial-of-Service (DDoS) attacks remains a challenge in cloud/edge/fog computing environments. It usually requires network middleboxes, such as deep packet inspectors (DPI), for detection task mostly. But clouds and fogs have native powerful telemetry systems that are not yet fully exploited for DDoS detection; and provide so much information that could aid attack identification tasks as well. Machine Learning (ML) algorithms can help one diving into the richness of cloud’s native data collection services, which have a multitude of metrics from both physical and virtual hosts. This paper evaluates the use of ML algorithms over datasets collected from a experimental testbed based on OpenStack. Controlled attack scenarios were used to investigate the ability of ML for tasks such as detecting and identifying SYN_Flood and GET_Flood DDoS attacks mixed, in different proportions, with legitimate clients. kNN and Random Forest ML algorithms were trained and tested, and for evaluation the metrics accuracy, recall, precision, and F1-score were used. Our experiments presented about 87% of accuracy in the detection of SYN_Flood and GET_Flood DDoS attacks, whereas Snort IDS mostly fails to detect the latter attack by processing the corresponding packet traces. Also, the detection of PING_Flood DDoS attack was tested without training as an initial evaluation towards the generalization of the proposal.

Introduction and Objective: To apply a machine learning (ML) algorithm to automatically identify the biological overnight fasting (BOF) period, using continuous glucose monitoring (CGM) profiles. Methods: Adults with prediabetes and early onset type 2 diabetes (T2D) enrolled in the NY-TREAT trial wore a blinded Abbott Freestyle Libre CGM and an Actigraph-GT3X accelerometer for ~14 days at baseline under free living conditions, except on days 13 and 14, when they received time-controlled research meals. A random forest ML algorithm identified the timing of last eating occasion (LEO) on a given day and the first eating occasion (FEO) the next morning. The BOF was defined as the period between LEO and FEO times, excluding a 3-hour postprandial response to the LEO. Results: 42 participants (61±7 years, 74% female, BMI 33±6 kg/m2, HbA1c 5.9±0.3%, 17% with T2D) provided 11±3 days of paired CGM and accelerometer data (mean±SD). The ML algorithm accurately identified LEO (30 [15,52] minutes) and FEO (30 [15,45] minutes) relative to controlled mealtimes on days 13 and 14 (median [IQR]). The duration of the BOF was 9.9 [8.9, 11.3] hours with an average BOF glucose level of 94 [87,101] mg/dL. Conclusion: We developed a novel ML approach to objectively estimate the BOF from CGM data, enabling a robust assessment of how BOF duration impacts glycemia in older adults with prediabetes and T2D. Disclosure S. Barua: None. D.A. Upadhyay: None. L.S. Santos-Baez: None. C. Popp: None. D.A. Díaz-Rizzolo: None. B. Laferrère: Consultant; UpToDate. Funding National Institute on Aging (NIA) R01AG065569; National Center for Advancing Translational Sciences (NCATS) UL1TR001873; National Heart, Lung, and Blood Institute (NHLBI) K99HL163474; National Institute of Nursing Research R01NR018916; American Heart Association 24SCEFIA1252353

يعد طرد الموظفين من المنظمات مشكلة خطيرة. يجب حل مشكلة تدوير الموظفين أو اطردهم من داخل المنظمة نظرًا لأن لها تأثيرًا سلبيًا على المنظمة. يعد الاكتشاف اليدوي لتسرب الموظفين أمرًا صعبًا للغاية، لذلك تم استخدام خوارزميات التعلم الآلي ML بشكل متكرر لاكتشاف تسرب الموظفين بالإضافة إلى تصنيف الموظفين وفقًا للاستبدالهم. باستخدام التعلم الآلي، بحثت دراسة واحدة فقط في تصنيف الموظفين حتى الآن. تم اقتراح نهج جديد لاتخاذ القرار متعدد المعايير MCDM إلى جانب مبدأ DE-PARETO لتصنيف الموظفين. يشار إلى هذا باسم مخطط SNEC . تم تصميم نموذج AHP-TOPSIS DE-PARETO PRINCIPLE (AHPTOPDE) الذي يستخدم نظام MCDM على مرحلتين لتصنيف الموظفين. في المرحلة الأولى، تم استخدام عملية التسلسل الهرمي التحليلي AHP لتعيين الأوزان النسبية لعوامل إنجاز الموظف. في المرحلة الثانية، تم استخدام TOPSIS للتعبير عن أهمية الموظفين في إجراء تصنيف الموظفين. تم تطبيق قاعدة 20-30-50 البسيطة في مبدأ DE PARETO لتصنيف الموظفين إلى ثلاث مجموعات رئيسية وهي الموظفون المتحمسون والسلوكيون والمضطربون. يتم بعد ذلك تطبيق خوارزمية الغابة العشوائية كخوارزمية أساسية لإطار عمل الموظفين المقترح للتنبؤ بخسارة الموظفين على أساس الفصل والذي يتم اختباره على مجموعة بيانات قياسية لنظام معلومات الموارد البشرية HRIS، ويتم تقييم النتائج التي تم الحصول عليها باستخدام طرق تعلم الآلة الأخرى. تتمتع خوارزمية Random Forest ML في مخطط SNEC بدقة إجمالية مماثلة أو أفضل قليلاً وMCC مع تعقيد زمني أقل مقارنةً بمخطط ECPR باستخدام خوارزمية CATBOOST

To construct a classification model of rupture status and to clarify the importance of morphologic variables and hemodynamic parameters on rupture status by applying a machine learning (ML) algorithm to morphologic and hemodynamic data of cerebral aneurysms. Between 2011 and 2019, 226 (112 ruptured and 114 unruptured) cerebral aneurysms in 188 consecutive patients were retrospectively analyzed with computational fluid dynamics (CFD). A random forest ML algorithm was applied to the results to create three classification models consisting of only morphologic variables (model 1), only hemodynamic parameters (model 2), and both morphologic variables and hemodynamic parameters (model 3). The accuracy of rupture status classification and the importance of each variable or parameter in the models were computed. The accuracy was 77.0% in model 1, 71.2% in model 2, and 78.3% in model 3. The three most important features were projection ratio, size ratio, and aspect ratio in model 1; low shear area ratio, oscillatory shear index, and oscillatory velocity index in model 2; and projection ratio, irregular shape, and size ratio in model 3. Classification models of rupture status of cerebral aneurysms were constructed by applying an ML algorithm to morphologic variables and hemodynamic parameters. The model worked with relatively high accuracy, in which projection ratio, irregular shape, and size ratio were important for the discrimination of ruptured aneurysms.Supplemental material is available for this article.© RSNA, 2020.

BackgroundTo develop a supervised machine learning (ML) algorithm predicting above- versus below-median overall survival (OS) from diffusion-weighted imaging-derived radiomic features in patients with pancreatic ductal adenocarcinoma (PDAC).MethodsOne hundred two patients with histopathologically proven PDAC were retrospectively assessed as training cohort, and 30 prospectively accrued and retrospectively enrolled patients served as independent validation cohort (IVC). Tumors were segmented on preoperative apparent diffusion coefficient (ADC) maps, and radiomic features were extracted. A random forest ML algorithm was fit to the training cohort and tested in the IVC. Histopathological subtype of tumor samples was assessed by immunohistochemistry in 21 IVC patients. Individual radiomic feature importance was evaluated by assessment of tree node Gini impurity decrease and recursive feature elimination. Fisher’s exact test, 95% confidence intervals (CI), and receiver operating characteristic area under the curve (ROC-AUC) were used.ResultsThe ML algorithm achieved 87% sensitivity (95% IC 67.3–92.7), 80% specificity (95% CI 74.0–86.7), and ROC-AUC 90% for the prediction of above- versus below-median OS in the IVC. Heterogeneity-related features were highly ranked by the model. Of the 21 patients with determined histopathological subtype, 8/9 patients predicted to experience below-median OS exhibited the quasi-mesenchymal subtype, whilst 11/12 patients predicted to experience above-median OS exhibited a non-quasi-mesenchymal subtype (p < 0.001).ConclusionML application to ADC radiomics allowed OS prediction with a high diagnostic accuracy in an IVC. The high overlap of clinically relevant histopathological subtypes with model predictions underlines the potential of quantitative imaging in PDAC pre-operative subtyping and prognosis.

Background: Aneurysmal subarachnoid hemorrhage (aSAH) results in significant mortality and disability, which is worsened by the development of Delayed Cerebral Ischemia (DCI). Tests to identify patients with DCI prospectively are needed. Objective: We created a machine learning (ML) system based on clinical variables to predict DCI in aSAH patients and to determine which variables have the most impact on DCI prediction. Methods: We performed a retrospective cohort study of aSAH patients from January 2006 to September 2014. The ML algorithm was trained on age, sex, HTN, diabetes, hyperlipidemia, CHF, CAD, smoking history, family history of aneurysm, Fisher Grade, Hunt and Hess score, and external ventricular drain (EVD) placement. Prediction outcome of the ML algorithm was DCI+, which was defined as new neurologic deterioration that could not be attributed to aneurysm re-bleeding, hydrocephalus, infection, seizure, hyponatremia or other metabolic abnormality. SHAP was used to explain and visualize the role of each feature’s contribution to the model prediction. Results: 500 aSAH patients were identified and 369 met inclusion criteria: 70 patients developed DCI (DCI+) and 299 did not (DCI-). Random Forest was selected for this project after a 5-fold cross validation. 276 cases (222 DCI- and 54 DCI+) were used for training and 93 cases (77 DCI- and 16 DCI+) were used for testing the algorithm. The Random Forest ML algorithm predicted DCI: Accuracy: 81.7%, Sensitivity: 12.5%, Specificity: 96.1%, PPV: 40%, and NPV: 84.1%. SHAP value demonstrated Age, EVD placement, Fisher Grade, and Hunt and Hess score, and HTN had the highest predictive values for DCI. Lower age, absence of hypertension, higher Hunt and Hess score, higher Fisher Grade, and EVD placement increased risk of DCI. Conclusion: ML models based upon clinical variable predict DCI with high specificity and modest accuracy. The addition of imaging or other biomarkers may improve the sensitivity of the ML algorithm.

BackgroundAs per the 2017 WHO fact sheet, Coronary Artery Disease (CAD) is the primary cause of death in the world, and accounts for 31% of total fatalities. The unprecedented 17.6 million deaths caused by CAD in 2016 underscores the urgent need to facilitate proactive and accelerated pre-emptive diagnosis. The innovative and emerging Machine Learning (ML) techniques can be leveraged to facilitate early detection of CAD which is a crucial factor in saving lives. The standard techniques like angiography, that provide reliable evidence are invasive and typically expensive and risky. In contrast, ML model generated diagnosis is non-invasive, fast, accurate and affordable. Therefore, ML algorithms can be used as a supplement or precursor to the conventional methods. This research demonstrates the implementation and comparative analysis of K Nearest Neighbor (k-NN) and Random Forest ML algorithms to achieve a targeted “At Risk” CAD classification using an emerging set of 35 cytokine biomarkers that are strongly indicative predictive variables that can be potential targets for therapy. To ensure better generalizability, mechanisms such as data balancing, repeated k-fold cross validation for hyperparameter tuning, were integrated within the models. To determine the separability efficacy of “At Risk” CAD versus Control achieved by the models, Area under Receiver Operating Characteristic (AUROC) metric is used which discriminates the classes by exhibiting tradeoff between the false positive and true positive rates.ResultsA total of 2 classifiers were developed, both built using 35 cytokine predictive features. The best AUROC score of .99 with a 95% Confidence Interval (CI) (.982,.999) was achieved by the Random Forest classifier using 35 cytokine biomarkers. The second-best AUROC score of .954 with a 95% Confidence Interval (.929,.979) was achieved by the k-NN model using 35 cytokines. A p-value of less than 7.481e-10 obtained by an independent t-test validated that Random Forest classifier was significantly better than the k-NN classifier with regards to the AUROC score.Presently, as large-scale efforts are gaining momentum to enable early, fast, reliable, affordable, and accessible detection of individuals at risk for CAD, the application of powerful ML algorithms can be leveraged as a supplement to conventional methods such as angiography. Early detection can be further improved by incorporating 65 novel and sensitive cytokine biomarkers. Investigation of the emerging role of cytokines in CAD can materially enhance the detection of risk and the discovery of mechanisms of disease that can lead to new therapeutic modalities.

Snowpack distribution in Arctic and alpine landscapes often occurs in repeating, year‐to‐year patterns due to local topographic, weather, and vegetation characteristics. Previous studies have suggested that with years of observational data, these snow distribution patterns can be statistically integrated into a snow process modeling workflow. Recent advances in snow hydrology and machine learning (ML) have increased our ability to predict snowpack distribution using in‐situ observations, remote sensing data sets, and simple landscape characteristics that can be easily obtained for most environments. Here, we propose a hybrid approach to couple a ML snow distribution pattern (MLSDP) map with a physics‐based, snow process model. We trained a random forest ML algorithm on tens of thousands of snow survey observations from a subarctic study area on the Seward Peninsula, Alaska, collected during peak snow water equivalent (SWE). We validated hybrid model outputs using in‐situ snow depth and SWE observations, as well as a light detection and ranging data set and a distributed temperature profiling sensor data set. When the hybrid results were compared with the physics‐based method, the hybrid method more accurately depicted the spatial patterns of the snowpack, areas of drifting snow, and years when no in‐situ observations were used in the random forest ML training data set. The hybrid method also showed improvements in root mean squared error at 61% of locations where time‐series estimations of snow depth were observed. These results can be applied to any physics‐based model to improve the snow distribution patterning to reflect observed conditions in high latitude and high elevation cold region environments.

Objective: Continuous glucose monitoring (CGM) provides a novel approach to monitor postprandial glucose responses (PPGR) in persons with gestational diabetes mellitus (GDM). We sought to develop a machine learning (ML) framework to identify PPGRs automatically from CGM data to address challenges of manual meal logging. Methods: Adults 18+ years diagnosed with GDM were enrolled at 24-32 gestational weeks and wore a blinded Freestyle Libre CGM and logged meals for up to 14 days each over 3 visits. A random forest ML algorithm was trained to identify the first meal of the day from raw daily CGM profiles. Comparing self-recorded and ML-predicted PPGRs, the primary outcome was difference in start time of PPGR while the secondary outcomes were the ratio of the corresponding 2-hr and 3-hr area under the PPGR curves. Results: We analyzed data from 19 participants with CGM and self-recorded meal logs (35 ± 6 years, 47% Asian or Black/African-American, 37% Hispanic/Latino). The ML algorithm predicted start time of PPGRs to within a median 30 [19,45] minutes of self-logged meal time (Fig 1). The median ratio of the corresponding 2-hr PPGR AUCs was 1.0 [0.98,1.03] and 3-hr AUCs was 1.0 [1.00,1.01]. Conclusion: An ML algorithm showed promising performance in identifying PPGRs accurately from CGM data in persons with GDM, enabling a new automated approach to meal logging and analyzing postprandial glucose patterns. Disclosure S. Barua: None. T. Sangmo: None. A. Khan: None. L. Berube: None. L. Li: None. S. Williams: None. T. Rosen: None. S. Rawal: Stock/Shareholder; Merck &amp; Co., Inc., LabCorp, Pfizer Inc. Research Support; National Institutes of Health. Stock/Shareholder; Bristol-Myers Squibb Company. Funding Rutgers SHP Dean's Intramural Grant

Machine learning approach in non-intrusive monitoring of tool wear evolution in massive CFRP automatic drilling processes in the aircraft industry

BackgroundFrailty is defined as a clinical state of increased vulnerability due to the age-associated decline of an individual’s physical function resulting in increased morbidity and mortality when exposed to acute stressors. Early identification and management can reverse individuals with frailty to being robust once more. However, we found no integration of machine learning (ML) tools and frailty screening and surveillance studies in Thailand despite the abundance of evidence of frailty assessment using ML globally and in Asia.ObjectiveWe propose an approach for early diagnosis of frailty in community-dwelling older individuals in Thailand using an ML model generated from individual characteristics and anthropometric data.MethodsDatasets including 2692 community-dwelling Thai older adults in Lampang from 2016 and 2017 were used for model development and internal validation. The derived models were externally validated with a dataset of community-dwelling older adults in Chiang Mai from 2021. The ML algorithms implemented in this study include the k-nearest neighbors algorithm, random forest ML algorithms, multilayer perceptron artificial neural network, logistic regression models, gradient boosting classifier, and linear support vector machine classifier.ResultsLogistic regression showed the best overall discrimination performance with a mean area under the receiver operating characteristic curve of 0.81 (95% CI 0.75‐0.86) in the internal validation dataset and 0.75 (95% CI 0.71‐0.78) in the external validation dataset. The model was also well-calibrated to the expected probability of the external validation dataset.ConclusionsOur findings showed that our models have the potential to be utilized as a screening tool using simple, accessible demographic and explainable clinical variables in Thai community-dwelling older persons to identify individuals with frailty who require early intervention to become physically robust.

Artificial intelligence (AI) is the simulation of human intelligence in machines that are programmed to think and learn like humans. AI has the potential to revolutionise the way that healthcare professionals diagnose, treat, and manage conditions affecting the female reproductive system. Machine learning (ML) is a subset of AI which deals with the development of algorithms and statistical models that enable computers to learn from and make predictions or decisions without being explicitly programmed to do so. Deep learning (DL) is a subfield of ML that utilises neural networks with multiple layers, known as deep neural networks (DNNs), to learn from data. DNNs are inspired by the structure and function of the human brain and are capable of automatically learning high-level features from raw data, such as images, audio and text. DL has been very successful in various applications such as image and speech recognition, natural language processing and computer vision. ML algorithms can be divided into three categories: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning algorithms are trained on a labelled dataset, where the desired output (label) is already known. Unsupervised learning algorithms are trained on an unlabelled dataset and are used to discover patterns or relationships in the data. Reinforcement learning algorithms are trained using a trial-and-error approach, where the agent receives a reward or penalty for its actions. The goal of reinforcement learning is to learn a policy that maximises the expected reward over time. AI and ML are increasingly being applied in the field of obstetrics and gynaecology, with the potential to improve diagnostic accuracy, patient outcomes, and efficiency of care. AI has been applied to the field of medicine for several decades. One of the earliest examples of AI in medicine was the development of MYCIN in the 1970s, a computer program that could diagnose bacterial infections and recommend appropriate antibiotic treatments. MYCIN was developed by a team at Stanford University led by Edward Shortliffe, and its success demonstrated the potential of AI in medical decision making. In the 1980s, AI-based expert systems such as DXplain, developed at Massachusetts General Hospital, were used to assist in the diagnosis of diseases. These early AI systems were based on rule-based systems and were limited in their capabilities. One of the earliest examples of AI was the development of computer-aided diagnostic systems for ultrasound images in the 1970s and 1980s. These systems were designed to assist radiologists in identifying fetal anomalies and other conditions. In recent years, there has been a renewed interest in the use of AI in obstetrics and gynaecology, driven by advances in ML and the availability of large amounts of data. One of the primary areas in which AI and ML are being used in obstetrics and gynaecology is in the analysis of imaging data, such as ultrasound and magnetic resonance imaging. AI algorithms can be trained to automatically identify and classify different structures in the images, such as the placenta or fetal organs, with high accuracy. Another area of focus is the use of AI to predict preterm birth. Researchers have used ML algorithms to analyse data from electronic health records and identify patterns that are associated with preterm birth. By analysing large datasets of patient information and outcomes, AI algorithms can identify patterns and risk factors that may not be apparent to human analysts. This can help to improve the prediction of obstetric outcomes and guide clinical decision making. In recent years, AI has also been applied in obstetrics and gynaecology for real-time monitoring of high-risk pregnancies and identifying fetal distress. These systems use ML algorithms to analyse data from fetal heart rate monitors and identify patterns that are associated with fetal distress. AI and ML are also being used to develop new tools for the management of gynaecological conditions, such as endometriosis and fibroids. These tools can be used to predict the progression of the disease and guide treatment decisions. One example of the use of AI in benign gynaecology is the development of computer-aided diagnostic systems for endometriosis. These systems use ML algorithms to analyse images of the pelvic region and identify the presence of endometrial tissue, which can be a sign of endometriosis. Another area where AI and ML are being applied is in the management of fibroids. ML algorithms are being used to analyse imaging data and predict the growth and behaviour of fibroids, which can aid in the development of personalised treatment plans. In the field of oncology, AI is being used to improve the accuracy and speed of cancer diagnosis. AI algorithms can analyse images of tissue samples to identify the presence of cancer cells and predict the likelihood of a positive outcome following treatment. AI algorithms can be trained to analyse images from pelvic scans and identify signs of ovarian cancer with high accuracy. In addition to these specific applications, AI and ML are also being used to improve the efficiency and organisation of care in obstetrics and gynaecology. For example, by analysing large amounts of clinical data, AI algorithms can be used to identify patients at high risk of complications, prioritise them for care and ensure that they receive the appropriate level of care in a timely manner. AI and ML have the potential to revolutionise the field of fertility and in vitro fertilisation (IVF). By using data from large patient populations, AI and ML algorithms can help identify patterns and predict outcomes that would be difficult for human experts to discern. This can lead to improvements in diagnosis, treatment planning, and overall success rates for patients undergoing IVF. One area where AI and ML are being applied is in the selection of embryos for transfer during IVF. By analysing images of embryos, AI and ML algorithms can predict which embryos are most likely to result in a successful pregnancy. Another area where AI and ML have shown potential is in the optimisation of culture conditions for embryos. This has the potential to improve the survival and development of embryos, leading to higher pregnancy rates. AI and ML are also being used to improve the timing of embryo transfer during IVF. By analysing data from patient medical histories, AI and ML algorithms can predict the optimal time for transfer to increase the chances of successful pregnancies. In addition to these applications, AI and ML are being used in other areas of fertility and IVF to improve patient outcomes. For example, AI and ML are being used to predict the likelihood of ovarian reserve, predict ovulation timing, and improve the efficiency and cost-effectiveness of fertility clinics. AI and ML are rapidly evolving fields that have the potential to revolutionise the field of surgery. These technologies can be used to assist surgeons in a variety of ways, from pre-operative planning to real-time guidance during procedures. One of the key areas where AI and ML are being applied in surgery is in image analysis. For example, algorithms can be used to automatically segment and identify structures in medical images, such as tumours or blood vessels. This can help surgeons plan procedures more accurately and reduce the risk of complications. Another area where AI and ML are being used in surgery is in the development of robotic systems. These systems can be programmed to perform specific tasks, such as suturing or cutting tissue, with a high degree of precision and accuracy. In addition, robotic systems can be equipped with sensors that provide real-time feedback to the surgeon, which can help to improve the outcome of the procedure. These systems can be programmed with advanced algorithms that allow them to make precise incisions, control bleeding, and minimise tissue damage. AI and ML can also be used to improve the efficiency and safety of surgical procedures. For example, algorithms can be trained to analyse data from vital signs monitors, such as heart rate and blood pressure, and alert surgeons to potential complications in real-time. AI and ML are also being used to assist with post-operative care. For example, algorithms can be used to analyse patient data and predict which patients are at risk of complications, such as infection or bleeding, allowing surgeons to take preventative measures. Overall, AI and ML have the potential to significantly improve the field of surgery by increasing accuracy and precision, reducing the risk of complications, and improving patient outcomes. As the technology continues to advance, it is likely that we will see an increasing number of AI-assisted surgical systems and applications in clinical practice. In gynaecology specifically, there is a scarcity of data and diversity in the data. This can lead to AI models that are not generalisable to certain populations or that make incorrect predictions for certain groups of patients. Overall, AI has the potential to improve the diagnosis and management of obstetrics and gynaecology conditions, and many studies have shown that AI systems can perform at least as well as human experts in several areas. However, it is important to note that AI and ML are still in the early stages of development in obstetrics and gynaecology and more research is needed to fully understand their potential benefits and limitations. Some of the key challenges facing the field include developing AI systems that can explain their decisions, improving the robustness of AI systems to adversarial attacks, and developing AI systems that can operate in a wide range of environments. However, it is important to note that AI is a complementary tool to the obstetrics and gynaecology specialist and it is not meant to replace human expertise. The preceding text is entirely a product of an AI system. The preceding review, Artificial Intelligence in Gynaecology: An Overview was composed and written by an evolutionary AI system, ChatGPT (Chat Generative Pre-trained Transformer). ChatGPT is an AI chatbot underpinned by the GPT architecture, an autoregressive language model that uses DL to produce human-like text. The system was trained on a dataset of over 500 GB of text data derived from books, articles, and websites prior to 2021. The system can engage in responsive dialogue, generate computer code, and produce coherent and fluent text.1 ChatGPT was conceived by OpenAI, an AI laboratory based in San Francisco, California, founded by Elon Musk and Sam Altman in 2015. Since its public release on November 30, 2022, the potential for use and misuse has exponentially grown,2 ultimately leading to the prohibition of the utilisation of AI systems by multiple organisations, including schools and universities. Prompted by this interest in AI, the aim of this study was to assess the capacity of ChatGPT to generate a scientific review. In January 2023, a multidisciplinary study group was assembled to develop the study protocol, confirm the methodology and approve the topic. This research was exempt from ethics review under National Health and Medical Research Council guidelines.3 ChatGPT was instructed to generate an narrative review based on dialogue with the lead author, AY. The input was informed by collaborative meetings of the study group over the study period. The study group nominated the topic, 'Artificial Intelligence in Gynaecology', but ChatGPT generated the title, structure and content for this paper. The study group defined the input parameters for ChatGPT and each AI output was reviewed by the authors for consistency and context, informing the next input. The dialogue thus became increasingly specific and refined in each iteration, as the initial general outline was expanded to include specific subheadings, academic language and academic references. The review was finalised from the ChatGPT output through an explicit composition protocol, limiting assembly to cut and paste, deletion to whole sentences (but not words) and conversion to Australian English. No grammatical or syntax correction was performed. The AI output was cross-referenced and verified by the study group. In this study, ChatGPT generated 7112 words in over 15 iterations, including 32 references. The output was restricted to the final review of 1809 words and nine unique references after removing duplicates4 and incorrect references (19). The final paper was submitted for blinded peer review. Thus, this study has demonstrated the capacity of an AI system, such as ChatGPT, to generate a scientific review through human academic instruction. AI is anticipated to expand the boundaries of evidence-based medicine through the potential of comprehensive analysis and summation of scientific publications. However, unlike systematic reviews or meta-analyses governed by explicit methodology, AI systems such as ChatGPT are the product of DL algorithms that are dependent upon the quality of the input to train the AI. Consequently, unlike systematic reviews, AI systems are bound by the bias, breadth, depth and quality of the training material. A dedicated medical AI would therefore be trained on an appropriate data set, such as the National Library of Medicine Medline/PubMed database. However, the volume of data is challenging: in 2022 alone, there were over 33 million citations equating to a dataset of almost 200 Gb for the minimum dataset. In contrast, ChatGPT has no external reference capabilities, such as access to the internet, search engines or any other sources of information outside of its own model. If forced outside of this framework, ChatGPT may generate plausible-sounding but incorrect or nonsensical responses.4 Most notably, pushing the AI to include references leads the system to generate bizarre fabrications.5 Our paper demonstrated that only 28% (9/32) of the references were authentic, although better than the 11% reported in a recent paper.6 In contrast to human writing, AI-generated content is more likely to be of limited depth, contain factual errors, fabricated references and repeat the instructions used to seed the output.7 The latter results in a formulaic language redundancy that all but identifies AI content. The human authors thus echo the conclusion of ChatGPT that AI is a complementary tool to the specialist and not meant to replace human expertise. For the moment. The authors report no conflicts of interest.