The use of artificial intelligence (AI) in medicine is currently an issue of great interest, especially with regard to the diagnostic or predictive analysis of medical data. As information technology in health care continues to develop, greater amounts of clinical medical data have been generated and will continue to expand every year. Diagnostic data, clinical trial data and data on medical staff behavioral health together form the largest data group. AI technology is now capable of deriving algorithms which can be used for diagnosis and treatment of disease and medical research. AI already has learning and self-correcting abilities and may further improve its accuracy based on feedback. An AI system can assist physicians by providing up-to-date medical information from journals, textbooks and clinical practices to inform proper patient care. Additionally, AI may help to reduce diagnostic and therapeutic errors which are inevitable in human clinical practice. AI systems extract information from large patient populations, allowing them to provide real-time advice on health risks and also predict health outcomes. AI devices are categorized into two major groups. The first group includes machine learning techniques that analyze structured data in an attempt to cluster patients’ traits, and consequently predict the probability of disease outcomes. The second group includes natural language processing methods that extract information from unstructured data, such as clinical notes and medical journals, to supplement and enrich structured medical data. The natural language processing procedures turn texts into machine-readable structured data, which can then be analyzed by machine learning techniques. AI is being employed in the medical field in at least four distinct ways: (1) in the assessment of risk of disease onset and in estimating treatment success prior to initiation; (2) in an attempt to manage or alleviate complications; (3) to assist with patient care during the active treatment or procedure phase; and (4) in research aimed at elucidating the pathology or mechanism of and/or the ideal treatment for a disease. The forecasting of health outcomes in different body systems (i.e. risk assesment for any disease) has been extensively investigated. Cardiovascular, breast, bone, cervix and endometrium have been the areas of interest in AI research in women’s health. Artificial neural networks (ANNs) and classification and regression trees for the prediction of endometrial cancer in postmenopausal women have been investigated. Similarly, AI has estimated the impact of human papillomavirus types in influencing the risk of cervical dysplasia recurrence. Extensive use of AI has been used in breast imaging where images from mammographic, sonographic and magnetic resonance imaging (MRI) were collected for study. The feasibility of automatically identifying normal digital mammography examinations with AI to reduce the reading workload of breast cancer screening was examined and it was found that incorporating an AI-based decision support system into ultasound image analysis improved diagnostic performance. Predictive models for osteoporosis have been constructed based on popular machine learning algorithms such as support vector machines, random forests, ANNs, and logistic regression based on simple surveys. These machine learning models were later compared to four conventional clinical decision tools: the Osteoporosis Self-assessment Tool, the Osteoporosis Risk Assessment Instrument, the Simple Calculated Osteoporosis Risk Estimation, and the Osteoporosis Index of Risk. Likewise, the application of an ANN in optimizing the Osteoporosis Self-Assessment Tool for Asians score has been reported. Moreover, it was identified that there are several methods in the use of AI to help the screening of groups at risk for osteoporosis or fractures. However, such systems were limited to a specific ethnic group, gender or age. Furthermore, a performance comparison of machine learning algorithms in predicting fragility fractures from MRI data was conducted where, among many classifiers, the random undersampling-boosted trees, logistic regression, and the linear discriminant are best for predicting osteoporotic fracture. Moreover, morphological, topological and mechanical bone features using AI methods were investigated. In a clinical trial, the performances of the Adaptive Neuro Fuzzy Inference System, support vector machines and genetic algorithms in classifying two populations of arthritic and osteoporotic bone samples were compared. Finally, AI was

Artificial intelligence and women's health.

Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a “one-stop center” synthesis and provide a holistic bird’s eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest.

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Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review

This chapter will cover the basic problems of women in South Asian countries. For this, a group survey is taken in India, Sri Lanka, Singapore, Malaysia, Thailand, Myanmar, and Nepal covering around 200 women about their basic problems in personal and professional lives and lifestyle and house management routine activities. The chapter will cover the basic segment of artificial intelligence and its support in maintaining the health parametric card for the household makers. The chapter will also assist the women in the kitchen with the help of artificial intelligence, especially in cooking and digital health menu selection. A special segment in the chapter will also address the cleanliness of the house with the help of A.I.-supporting tools and gadgets. Last but not the least, the chapter will also address the security of the household makers especially in day hours when they are alone in the houses. The chapter will be an innovative research to understand the better half of the society and to address their day-to-day problems with technological interventions. 

Artificial Intelligence for the Comprehensive Development of Household Makers (Especially in South Asian Countries)

The examination of construction workers’ safety behavior (CWSB) is a critical factor in mitigating the occurrence of construction accidents. This study conducted a scientometric and critical review of 3280 CWSB-related articles indexed in the Web of Science database. Scientometric analyses (e.g., co-authorship, co-word, co-citation, citation-burst analysis, and clustering) objectively visualized the current research landscape, while the critical review identified key research topics and challenges within the CWSB research. The findings reveal that over half of CWSB research originates from three countries: the USA, China, and Australia. Concurrently, the Hong Kong Polytechnic University, the City University of Hong Kong, and the University of Michigan stand out as the most productive institutions in the CWSB domain. It is noteworthy that China shows a high burst strength in 2022–2023, indicating that the development of the CWSB field in China is gaining global attention. The terms ‘performance’, ‘model’, and ‘management’ appear with the highest frequency, while keywords such as ‘deep learning’ and ‘simulation’ have experienced an increase in citations in recent years. Furthermore, 13 co-citation clusters were identified, with cluster analysis and critical reviews converging on three principal research themes: ‘conception and dimension’, ‘critical influence factors’, and ‘emerging technologies’. This study also proposes three research gaps and potential avenues for future investigation, including a comprehensive understanding of CWSB impact mechanisms, the long-term efficacy of safety interventions, and the incorporation of novel technologies into safety programs. This review offers valuable insights into extant CWSB research and pinpoints emerging trends within this research area. It provides essential information for industry policymakers, researchers, and practitioners in the global CWSB context and assists stakeholders in identifying and comprehending trends and patterns.

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Science Mapping the Knowledge Domain of Construction Workers’ Safety Behavior

OBJECTIVE. Data engineering is the foundation of effective machine learning model development and research. The accuracy and clinical utility of machine learning models fundamentally depend on the ...

https://www.ajronline.org/doi/pdf/10.2214/AJR.18.20464

Data Engineering for Machine Learning in Women's Imaging and Beyond.

Background With the increasing interest of academics in the application of artificial intelligence to sepsis, thousands of papers on this field had been published in the past few decades. It is difficult for researchers to understand the themes and latest research frontiers in this field from a multi-dimensional perspective. Consequently, the purpose of this study is to analyze the relevant literature in the application of artificial intelligence to sepsis through bibliometrics software, so as to better understand the development status, study the core hotspots and future development trends of this field. Methods We collected relevant publications in the application of artificial intelligence to sepsis from the Web of Science Core Collection in 2000 to 2021. The type of publication was limited to articles and reviews, and language was limited to English. Research cooperation network, journals, cited references, keywords in this field were visually analyzed by using CiteSpace, VOSviewer, and COOC software. Results A total of 8,481 publications in the application of artificial intelligence to sepsis between 2000 and 2021 were included, involving 8,132 articles and 349 reviews. Over the past 22 years, the annual number of publications had gradually increased exponentially. The USA was the most productive country, followed by China. Harvard University, Schuetz, Philipp, and Intensive Care Medicine were the most productive institution, author, and journal, respectively. Vincent, Jl and Critical Care Medicine were the most cited author and cited journal, respectively. Several conclusions can be drawn from the analysis of the cited references, including the following: screening and identification of sepsis biomarkers, treatment and related complications of sepsis, and precise treatment of sepsis. Moreover, there were a spike in searches relating to machine learning, antibiotic resistance and accuracy based on burst detection analysis. Conclusion This study conducted a comprehensive and objective analysis of the publications on the application of artificial intelligence in sepsis. It can be predicted that precise treatment of sepsis through machine learning technology is still research hotspot in this field.

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Research frontiers and trends in the application of artificial intelligence to sepsis: A bibliometric analysis

Pseudomonas aeruginosa is a ubiquitous opportunistic bacterial pathogen, which is a leading cause of nosocomial pneumonia. Early identification of the risk factors is urgently needed for severe infection patients with P. aeruginosa. However, no detailed relevant investigation based on machine learning has been reported, and little research has focused on exploring relationships between key risk clinical variables and clinical outcome of patients. In this study, we collected 571 severe infections with P. aeruginosa patients admitted to the Xijing Hospital of the Fourth Military Medical University from January 2010 to July 2021. Basic clinical information, clinical signs and symptoms, laboratory indicators, bacterial culture, and drug related were recorded. Machine learning algorithm of XGBoost was applied to build a model for predicting mortality risk of P. aeruginosa infection in severe patients. The performance of XGBoost model (AUROC = 0.94 ± 0.01, AUPRC = 0.94 ± 0.03) was greater than the performance of support vector machine (AUROC = 0.90 ± 0.03, AUPRC = 0.91 ± 0.02) and random forest (AUROC = 0.93 ± 0.03, AUPRC = 0.89 ± 0.04). This study also aimed to interpret the model and to explore the impact of clinical variables. The interpretation analysis highlighted the effects of age, high-alert drugs, and the number of drug varieties. Further stratification clarified the necessity of different treatment for severe infection for different populations.

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A prediction and interpretation machine learning framework of mortality risk among severe infection patients with pseudomonas aeruginosa

Vancomycin-associated acute kidney injury (AKI) continues to pose a major challenge to both patients and healthcare providers. The purpose of this study is to construct a machine learning framework for stratified predicting and interpreting vancomycin-associated AKI. Our study is a retrospective analysis of medical records of 724 patients who have received vancomycin therapy from 1 January 2015 through 30 September 2020. The basic clinical information, vancomycin dosage and days, comorbidities and medication, laboratory indicators of the patients were recorded. Machine learning algorithm of XGBoost was used to construct a series risk prediction model for vancomycin-associated AKI in different underlying diseases. The vast majority of sub-model performed best on the corresponding sub-dataset. Additionally, the aim of this study was to explain each model and to explore the influence of clinical variables on prediction. As the results of the analysis showed that in addition to the common indicators (serum creatinine and creatinine clearance rate), some other underappreciated indicators such as serum cystatin and cumulative days of vancomycin administration, weight and age, neutrophils and hemoglobin were the risk factors for cancer, diabetes mellitus, heptic insufficiency respectively. Stratified analysis of the comorbidities in patients with vancomycin-associated AKI further confirmed the necessity for different patient populations to be studied.

Chen Cui

Papers

Data Engineering for Machine Learning in Women's Imaging and Beyond.

Research frontiers and trends in the application of artificial intelligence to sepsis: A bibliometric analysis

A prediction and interpretation machine learning framework of mortality risk among severe infection patients with pseudomonas aeruginosa

Analysis of a machine learning–based risk stratification scheme for acute kidney injury in vancomycin