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Sensors (Basel, Switzerland) Jun 2021Plant phenomics has been rapidly advancing over the past few years. This advancement is attributed to the increased innovation and availability of new technologies which... (Review)
Review
Plant phenomics has been rapidly advancing over the past few years. This advancement is attributed to the increased innovation and availability of new technologies which can enable the high-throughput phenotyping of complex plant traits. The application of artificial intelligence in various domains of science has also grown exponentially in recent years. Notably, the computer vision, machine learning, and deep learning aspects of artificial intelligence have been successfully integrated into non-invasive imaging techniques. This integration is gradually improving the efficiency of data collection and analysis through the application of machine and deep learning for robust image analysis. In addition, artificial intelligence has fostered the development of software and tools applied in field phenotyping for data collection and management. These include open-source devices and tools which are enabling community driven research and data-sharing, thereby availing the large amounts of data required for the accurate study of phenotypes. This paper reviews more than one hundred current state-of-the-art papers concerning AI-applied plant phenotyping published between 2010 and 2020. It provides an overview of current phenotyping technologies and the ongoing integration of artificial intelligence into plant phenotyping. Lastly, the limitations of the current approaches/methods and future directions are discussed.
Topics: Artificial Intelligence; Machine Learning; Phenomics; Phenotype; Software
PubMed: 34202291
DOI: 10.3390/s21134363 -
Methods in Molecular Biology (Clifton,... 2022The discovery and development of drugs is a long and expensive process with a high attrition rate. Computational drug discovery contributes to ligand discovery and... (Review)
Review
The discovery and development of drugs is a long and expensive process with a high attrition rate. Computational drug discovery contributes to ligand discovery and optimization, by using models that describe the properties of ligands and their interactions with biological targets. In recent years, artificial intelligence (AI) has made remarkable modeling progress, driven by new algorithms and by the increase in computing power and storage capacities, which allow the processing of large amounts of data in a short time. This review provides the current state of the art of AI methods applied to drug discovery, with a focus on structure- and ligand-based virtual screening, library design and high-throughput analysis, drug repurposing and drug sensitivity, de novo design, chemical reactions and synthetic accessibility, ADMET, and quantum mechanics.
Topics: Artificial Intelligence; Deep Learning; Drug Design; Ligands; Machine Learning
PubMed: 34731478
DOI: 10.1007/978-1-0716-1787-8_16 -
Neuron Sep 2019Despite enormous progress in machine learning, artificial neural networks still lag behind brains in their ability to generalize to new situations. Given identical... (Review)
Review
Despite enormous progress in machine learning, artificial neural networks still lag behind brains in their ability to generalize to new situations. Given identical training data, differences in generalization are caused by many defining features of a learning algorithm, such as network architecture and learning rule. Their joint effect, called "inductive bias," determines how well any learning algorithm-or brain-generalizes: robust generalization needs good inductive biases. Artificial networks use rather nonspecific biases and often latch onto patterns that are only informative about the statistics of the training data but may not generalize to different scenarios. Brains, on the other hand, generalize across comparatively drastic changes in the sensory input all the time. We highlight some shortcomings of state-of-the-art learning algorithms compared to biological brains and discuss several ideas about how neuroscience can guide the quest for better inductive biases by providing useful constraints on representations and network architecture.
Topics: Algorithms; Artificial Intelligence; Bias; Brain; Deep Learning; Generalization, Psychological; Humans; Machine Learning; Neural Networks, Computer; Neurosciences
PubMed: 31557461
DOI: 10.1016/j.neuron.2019.08.034 -
Advances in Surgery Sep 2020
Review
Topics: Algorithms; Artificial Intelligence; Electronic Health Records; Humans; Image Processing, Computer-Assisted; Machine Learning; Natural Language Processing; Neural Networks, Computer; Physician's Role; Risk Assessment; Surgical Procedures, Operative; United States
PubMed: 32713441
DOI: 10.1016/j.yasu.2020.05.010 -
Orthodontics & Craniofacial Research Dec 2021
Topics: Artificial Intelligence; Machine Learning; Orthodontics
PubMed: 34825474
DOI: 10.1111/ocr.12543 -
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 -
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 -
The Lancet. Digital Health Jul 2020
Review
Topics: Artificial Intelligence; Data Analysis; Delivery of Health Care; Humans
PubMed: 33328093
DOI: 10.1016/S2589-7500(20)30141-2 -
Diagnostic and Interventional Imaging Jan 2023In the elective field of adrenal imaging, artificial intelligence (AI) can be used for adrenal lesion detection, characterization, hypersecreting syndrome management and... (Review)
Review
In the elective field of adrenal imaging, artificial intelligence (AI) can be used for adrenal lesion detection, characterization, hypersecreting syndrome management and patient follow-up. Although a perfect AI tool that includes all required steps from detection to analysis does not exist yet, multiple AI algorithms have been developed and tested with encouraging results. However, AI in this setting is still at an early stage. In this regard, most published studies about AI in adrenal gland imaging report preliminary results that do not have yet daily applications in clinical practice. In this review, recent developments and current results of AI in the field of adrenal imaging are presented. Limitations and future perspectives of AI are discussed.
Topics: Humans; Artificial Intelligence; Machine Learning; Deep Learning; Algorithms; Diagnostic Imaging
PubMed: 36163169
DOI: 10.1016/j.diii.2022.09.003 -
Current Opinion in Ophthalmology Sep 2021Myopia is one of the leading causes of visual impairment, with a projected increase in prevalence globally. One potential approach to address myopia and its... (Review)
Review
PURPOSE OF REVIEW
Myopia is one of the leading causes of visual impairment, with a projected increase in prevalence globally. One potential approach to address myopia and its complications is early detection and treatment. However, current healthcare systems may not be able to cope with the growing burden. Digital technological solutions such as artificial intelligence (AI) have emerged as a potential adjunct for myopia management.
RECENT FINDINGS
There are currently four significant domains of AI in myopia, including machine learning (ML), deep learning (DL), genetics and natural language processing (NLP). ML has been demonstrated to be a useful adjunctive for myopia prediction and biometry for cataract surgery in highly myopic individuals. DL techniques, particularly convoluted neural networks, have been applied to various image-related diagnostic and predictive solutions. Applications of AI in genomics and NLP appear to be at a nascent stage.
SUMMARY
Current AI research is mainly focused on disease classification and prediction in myopia. Through greater collaborative research, we envision AI will play an increasingly critical role in big data analysis by aggregating a greater variety of parameters including genomics and environmental factors. This may enable the development of generalizable adjunctive DL systems that could help realize predictive and individualized precision medicine for myopic patients.
Topics: Artificial Intelligence; Deep Learning; Forecasting; Genomics; Humans; Machine Learning; Myopia; Natural Language Processing; Neural Networks, Computer
PubMed: 34310401
DOI: 10.1097/ICU.0000000000000791