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Medicina 2022
Topics: Algorithms; Artificial Intelligence; Humans; Machine Learning; Neoplasms; Technology
PubMed: 36220044
DOI: No ID Found -
World Journal of Gastroenterology May 2022Given the breakthroughs in key technologies, such as image recognition, deep learning and neural networks, artificial intelligence (AI) continues to be increasingly... (Review)
Review
Given the breakthroughs in key technologies, such as image recognition, deep learning and neural networks, artificial intelligence (AI) continues to be increasingly developed, leading to closer and deeper integration with an increasingly data-, knowledge- and brain labor-intensive medical industry. As society continues to advance and individuals become more aware of their health needs, the problems associated with the aging of the population are receiving increasing attention, and there is an urgent demand for improving medical technology, prolonging human life and enhancing health. Digestive system diseases are the most common clinical diseases and are characterized by complex clinical manifestations and a general lack of obvious symptoms in the early stage. Such diseases are very difficult to diagnose and treat. In recent years, the incidence of diseases of the digestive system has increased. As AI applications in the field of health care continue to be developed, AI has begun playing an important role in the diagnosis and treatment of diseases of the digestive system. In this paper, the application of AI in assisted diagnosis and the application and prospects of AI in malignant and benign digestive system diseases are reviewed.
Topics: Artificial Intelligence; Deep Learning; Gastrointestinal Diseases; Humans; Neural Networks, Computer
PubMed: 35721881
DOI: 10.3748/wjg.v28.i20.2152 -
International Journal of Surgery... Dec 2023
Topics: Humans; Artificial Intelligence; Deep Learning; Neural Networks, Computer
PubMed: 37720927
DOI: 10.1097/JS9.0000000000000748 -
Archives of Pathology & Laboratory... May 2017
Topics: Artificial Intelligence; Deep Learning; Forecasting; Humans; Pathologists
PubMed: 28447905
DOI: 10.5858/arpa.2016-0593-ED -
Computational Intelligence and... 2022This study aims to explore the application of artificial intelligence (AI) and network technology in teaching. By studying the AI-based smart classroom teaching mode and...
This study aims to explore the application of artificial intelligence (AI) and network technology in teaching. By studying the AI-based smart classroom teaching mode and the advantages and disadvantages of network teaching using network technology and taking the mathematics classroom as an example, this study makes an intelligent analysis of the questioning link of classroom teachers in the teaching process. For the questions raised by teachers, the network classification models of convolutional neural network (CNN) and long short-term memory (LSTM) are used to classify the questions according to the content and types of questions and carry out experimental verification. The results show that the overall performance of the CNN model is better than that of the LSTM model in the classification results of the teacher's question content dimension. CNN has higher accuracy, and the classification accuracy of essential knowledge points reaches 86.3%. LSTM is only 79.2%, and CNN improves by 8.96%. In the classification results of teacher question types, CNN has higher accuracy. The classification accuracy of the prompt question is the highest, reaching 87.82%. LSTM is only 83.2%, and CNN improves by 4.95%. CNN performs better in teacher question classification results.
Topics: Artificial Intelligence; Memory, Long-Term; Neural Networks, Computer
PubMed: 35720947
DOI: 10.1155/2022/1778562 -
Journal of the American Society of... Sep 2020Artificial intelligence (AI) is emerging as a key component in diagnostic medical imaging, including echocardiography. AI with deep learning has already been used with... (Review)
Review
Artificial intelligence (AI) is emerging as a key component in diagnostic medical imaging, including echocardiography. AI with deep learning has already been used with automated view labeling, measurements, and interpretation. As the development and use of AI in echocardiography increase, potential concerns may be raised by cardiac sonographers and the profession. This report, from a sonographer's perspective, focuses on defining AI, the basics of the technology, identifying some current applications of AI, and how the use of AI may improve patient care in the future.
Topics: Artificial Intelligence; Deep Learning; Echocardiography; Forecasting; Humans; Machine Learning
PubMed: 32536431
DOI: 10.1016/j.echo.2020.04.025 -
Current Cardiology Reviews 2022Artificial intelligence technology is emerging as a promising entity in cardiovascular medicine, potentially improving diagnosis and patient care. In this article, we... (Review)
Review
Artificial intelligence technology is emerging as a promising entity in cardiovascular medicine, potentially improving diagnosis and patient care. In this article, we review the literature on artificial intelligence and its utility in cardiology. We provide a detailed description of concepts of artificial intelligence tools like machine learning, deep learning, and cognitive computing. This review discusses the current evidence, application, prospects, and limitations of artificial intelligence in cardiology.
Topics: Algorithms; Artificial Intelligence; Cardiology; Humans; Machine Learning
PubMed: 34802407
DOI: 10.2174/1573403X17666211119102220 -
JACC. Heart Failure Jul 2020
Topics: Algorithms; Artificial Intelligence; Heart Failure; Humans; Intelligence; Machine Learning
PubMed: 32616167
DOI: 10.1016/j.jchf.2020.06.002 -
Physica Medica : PM : An International... Mar 2021Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence of disruptive technical advances and impressive experimental results, notably... (Review)
Review
Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence of disruptive technical advances and impressive experimental results, notably in the field of image analysis and processing. In medicine, specialties where images are central, like radiology, pathology or oncology, have seized the opportunity and considerable efforts in research and development have been deployed to transfer the potential of AI to clinical applications. With AI becoming a more mainstream tool for typical medical imaging analysis tasks, such as diagnosis, segmentation, or classification, the key for a safe and efficient use of clinical AI applications relies, in part, on informed practitioners. The aim of this review is to present the basic technological pillars of AI, together with the state-of-the-art machine learning methods and their application to medical imaging. In addition, we discuss the new trends and future research directions. This will help the reader to understand how AI methods are now becoming an ubiquitous tool in any medical image analysis workflow and pave the way for the clinical implementation of AI-based solutions.
Topics: Algorithms; Artificial Intelligence; Machine Learning; Radiology; Technology
PubMed: 33979715
DOI: 10.1016/j.ejmp.2021.04.016 -
Anesthesiology Mar 2018
Topics: Artificial Intelligence; Deep Learning; Propofol; Remifentanil
PubMed: 29166324
DOI: 10.1097/ALN.0000000000001984