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1.
Curr Opin Ophthalmol ; 31(5): 447-453, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32694268

RESUMO

PURPOSE OF REVIEW: To highlight artificial intelligence applications in ophthalmology during the COVID-19 pandemic that can be used to: describe ocular findings and changes correlated with COVID-19; extract information from scholarly articles on SARS-CoV-2 and COVID-19 specific to ophthalmology; and implement efficient patient triage and telemedicine care. RECENT FINDINGS: Ophthalmology has been leading in artificial intelligence and technology applications. With medical imaging analysis, pixel-annotated distinguishable features on COVID-19 patients may help with noninvasive diagnosis and severity outcome predictions. Using natural language processing (NLP) and data integration methods, topic modeling on more than 200 ophthalmology-related articles on COVID-19 can summarize ocular manifestations, viral transmission, treatment strategies, and patient care and practice management. Artificial intelligence for telemedicine applications can address the high demand, prioritize and triage patients, as well as improve at home-monitoring devices and secure data transfers. SUMMARY: COVID-19 is significantly impacting the way we are delivering healthcare. Given the already successful implementation of artificial intelligence applications and telemedicine in ophthalmology, we expect that these systems will be embraced more as tools for research, education, and patient care.


Assuntos
Inteligência Artificial/tendências , Betacoronavirus , Infecções por Coronavirus/epidemiologia , Pneumonia Viral/epidemiologia , Humanos , Oftalmologia , Pandemias , Telemedicina/tendências
2.
Proc Natl Acad Sci U S A ; 117(30): 17491-17498, 2020 07 28.
Artigo em Inglês | MEDLINE | ID: mdl-32694210

RESUMO

The potential benefits of autonomous systems are obvious. However, there are still major issues to be dealt with before developing such systems becomes a commonplace engineering practice, with accepted and trustworthy deliverables. We argue that a solid, evolving, publicly available, community-controlled foundation for developing next-generation autonomous systems is a must, and term the desired foundation "autonomics." We focus on three main challenges: 1) how to specify autonomous system behavior in the face of unpredictability; 2) how to carry out faithful analysis of system behavior with respect to rich environments that include humans, physical artifacts, and other systems; and 3) how to build such systems by combining executable modeling techniques from software engineering with artificial intelligence and machine learning.


Assuntos
Inteligência Artificial/tendências , Humanos
3.
IEEE Pulse ; 11(3): 2-6, 2020.
Artigo em Inglês | MEDLINE | ID: covidwho-607664

RESUMO

An estimated 792 million people live with mental health disorders worldwide-more than one in ten people-and this number is expected to grow in the shadow of the Coronavirus disease 2019 (COVID-19) pandemic. Unfortunately, there aren't enough mental health professionals to treat all these people. Can artificial intelligence (AI) help? While many psychiatrists have different views on this question, recent developments suggest AI may change the practice of psychiatry for both clinicians and patients.


Assuntos
Inteligência Artificial/tendências , Psiquiatria/tendências , Betacoronavirus , Infecções por Coronavirus/complicações , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/psicologia , Humanos , Transtornos Mentais/diagnóstico , Transtornos Mentais/etiologia , Transtornos Mentais/terapia , Aplicativos Móveis , Pandemias , Pneumonia Viral/complicações , Pneumonia Viral/epidemiologia , Pneumonia Viral/psicologia , Psicoterapia/métodos , Psicoterapia/tendências , Smartphone
4.
Br J Radiol ; 93(1111): 20200113, 2020 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-32496816

RESUMO

MRI developed during the last half-century from a very basic concept to an indispensable non-ionising medical imaging technique that has found broad application in diagnostics, therapy control and far beyond. Due to its excellent soft-tissue contrast and the huge variety of accessible tissue- and physiological-parameters, MRI is often preferred to other existing modalities. In the course of its development, MRI underwent many substantial transformations. From the beginning, starting as a proof of concept, much effort was expended to develop the appropriate basic scanning technology and methodology, and to establish the many clinical contrasts (e.g., T1, T2, flow, diffusion, water/fat, etc.) that MRI is famous for today. Beyond that, additional prominent innovations to the field have been parallel imaging and compressed sensing, leading to significant scanning time reductions, and the move towards higher static magnetic field strengths, which led to increased sensitivity and improved image quality. Improvements in workflow and the use of artificial intelligence are among many current trends seen in this field, paving the way for a broad use of MRI. The 125th anniversary of the BJR is a good point to reflect on all these changes and developments and to offer some slightly speculative ideas as to what the future may bring.


Assuntos
Invenções/tendências , Imagem por Ressonância Magnética/tendências , Inteligência Artificial/tendências , Meios de Contraste , Aprendizado Profundo/tendências , Humanos , Imagem por Ressonância Magnética/instrumentação , Imagem por Ressonância Magnética/métodos , Magnetismo , Fluxo de Trabalho
5.
IEEE Pulse ; 11(3): 2-6, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32559160

RESUMO

An estimated 792 million people live with mental health disorders worldwide-more than one in ten people-and this number is expected to grow in the shadow of the Coronavirus disease 2019 (COVID-19) pandemic. Unfortunately, there aren't enough mental health professionals to treat all these people. Can artificial intelligence (AI) help? While many psychiatrists have different views on this question, recent developments suggest AI may change the practice of psychiatry for both clinicians and patients.


Assuntos
Inteligência Artificial/tendências , Psiquiatria/tendências , Betacoronavirus , Infecções por Coronavirus/complicações , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/psicologia , Humanos , Transtornos Mentais/diagnóstico , Transtornos Mentais/etiologia , Transtornos Mentais/terapia , Aplicativos Móveis , Pandemias , Pneumonia Viral/complicações , Pneumonia Viral/epidemiologia , Pneumonia Viral/psicologia , Psicoterapia/métodos , Psicoterapia/tendências , Smartphone
6.
Nat Biotechnol ; 38(7): 788-789, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-32555347
8.
Pediatrics ; 145(Suppl 2): S186-S194, 2020 05.
Artigo em Inglês | MEDLINE | ID: mdl-32358210

RESUMO

As avid users of technology, adolescents are a key demographic to engage when designing and developing technology applications for health. There are multiple opportunities for improving adolescent health, from promoting preventive behaviors to providing guidance for adolescents with chronic illness in supporting treatment adherence and transition to adult health care systems. This article will provide a brief overview of current technologies and then highlight new technologies being used specifically for adolescent health, such as artificial intelligence, virtual and augmented reality, and machine learning. Because there is paucity of evidence in this field, we will make recommendations for future research.


Assuntos
Saúde do Adolescente , Desenvolvimento Industrial , Adolescente , Saúde do Adolescente/tendências , Serviços de Saúde do Adolescente/tendências , Inteligência Artificial/tendências , Realidade Aumentada , Assistência à Saúde/tendências , Previsões , Promoção da Saúde/tendências , Humanos , Desenvolvimento Industrial/tendências , Aprendizado de Máquina/tendências , Pesquisa/tendências , Estados Unidos , Realidade Virtual
9.
Mayo Clin Proc ; 95(5): 1015-1039, 2020 05.
Artigo em Inglês | MEDLINE | ID: mdl-32370835

RESUMO

Artificial intelligence (AI) is a nontechnical, popular term that refers to machine learning of various types but most often to deep neural networks. Cardiology is at the forefront of AI in medicine. For this review, we searched PubMed and MEDLINE databases with no date restriction using search terms related to AI and cardiology. Articles were selected for inclusion on the basis of relevance. We highlight the major achievements in recent years in nearly all areas of cardiology and underscore the mounting evidence suggesting how AI will take center stage in the field. Artificial intelligence requires a close collaboration among computer scientists, clinical investigators, clinicians, and other users in order to identify the most relevant problems to be solved. Best practices in the generation and implementation of AI include the selection of ideal data sources, taking into account common challenges during the interpretation, validation, and generalizability of findings, and addressing safety and ethical concerns before final implementation. The future of AI in cardiology and in medicine in general is bright as the collaboration between investigators and clinicians continues to excel.


Assuntos
Inteligência Artificial/tendências , Cardiologia/métodos , Cardiopatias , Previsões , Cardiopatias/diagnóstico , Cardiopatias/terapia , Humanos
14.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi ; 37(2): 230-235, 2020 Apr 25.
Artigo em Chinês | MEDLINE | ID: mdl-32329274

RESUMO

Recently, artificial intelligence (AI) has been widely applied in the diagnosis and treatment of urinary diseases with the development of data storage, image processing, pattern recognition and machine learning technologies. Based on the massive biomedical big data of imaging and histopathology, many urinary system diseases (such as urinary tumor, urological calculi, urinary infection, voiding dysfunction and erectile dysfunction) will be diagnosed more accurately and will be treated more individualizedly. However, most of the current AI diagnosis and treatment are in the pre-clinical research stage, and there are still some difficulties in the wide application of AI. This review mainly summarizes the recent advances of AI in the diagnosis of prostate cancer, bladder cancer, kidney cancer, urological calculi, frequent micturition and erectile dysfunction, and discusses the future potential and existing problems.


Assuntos
Inteligência Artificial/tendências , Diagnóstico por Computador/tendências , Doenças Urológicas/diagnóstico , Humanos , Processamento de Imagem Assistida por Computador
15.
PLoS Comput Biol ; 16(4): e1007792, 2020 04.
Artigo em Inglês | MEDLINE | ID: mdl-32275707

RESUMO

Until date, several machine learning approaches have been proposed for the dynamic modeling of temporal omics data. Although they have yielded impressive results in terms of model accuracy and predictive ability, most of these applications are based on "Black-box" algorithms and more interpretable models have been claimed by the research community. The recent eXplainable Artificial Intelligence (XAI) revolution offers a solution for this issue, were rule-based approaches are highly suitable for explanatory purposes. The further integration of the data mining process along with functional-annotation and pathway analyses is an additional way towards more explanatory and biologically soundness models. In this paper, we present a novel rule-based XAI strategy (including pre-processing, knowledge-extraction and functional validation) for finding biologically relevant sequential patterns from longitudinal human gene expression data (GED). To illustrate the performance of our pipeline, we work on in vivo temporal GED collected within the course of a long-term dietary intervention in 57 subjects with obesity (GSE77962). As validation populations, we employ three independent datasets following the same experimental design. As a result, we validate primarily extracted gene patterns and prove the goodness of our strategy for the mining of biologically relevant gene-gene temporal relations. Our whole pipeline has been gathered under open-source software and could be easily extended to other human temporal GED applications.


Assuntos
Biologia Computacional/métodos , Mineração de Dados/métodos , Perfilação da Expressão Gênica/métodos , Algoritmos , Inteligência Artificial/tendências , Bases de Dados Genéticas , Expressão Gênica/genética , Humanos , Estudos Longitudinais , Aprendizado de Máquina , Obesidade/genética , Software , Transcriptoma/genética
16.
Neuron ; 105(3): 413-415, 2020 02 05.
Artigo em Inglês | MEDLINE | ID: mdl-32027832

RESUMO

An international group of researchers met in November 2019 in Beijing to explore the intersection of neuroscience and AI. The aim was to offer a fertile ground for stimulating discussions and ideas, including issues such as policy making and the future of neuroscience and AI across the globe.


Assuntos
Inteligência Artificial/tendências , Congressos como Assunto/tendências , Inteligência/fisiologia , Invenções/tendências , Pequim , Humanos , Neurociências/métodos , Neurociências/tendências
18.
J Nurs Adm ; 50(3): 125-127, 2020 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-32068622

RESUMO

As systems evolve over time, their natural tendency is to become increasingly more complex. Studies in the field of complex systems have generated new perspectives on the application of management strategies in health systems. Much of this research appears as a natural extension of the cross-disciplinary field of systems theory. Since writing my 1st article for Managing Organizational Complexity in 2004, much has happened to further our understanding of complexity in healthcare systems. The growth of new computational methods in the fields of data science and data analytics has allowed scientists to identify signals or patterns in large complex data sets (big data) that in the past were seemingly hidden. Rather than relying on historical statistical methods to infer outcomes, these advanced methods combined with increased computer processing power allow machines to learn the structure of data and create artificial intelligence (AI). In our ongoing efforts to find solutions for complex healthcare problems, AI is becoming more and more an accepted method. The purpose of this edition of Managing Organizational Complexity is to define AI and machine learning, discuss the recent resurgence of AI, and then provide examples of how AI can provide value to healthcare with an emphasis on nursing.


Assuntos
Inteligência Artificial/tendências , Eficiência Organizacional/tendências , Enfermeiras Administradoras/tendências , Cuidados de Enfermagem/tendências , Humanos , Recursos Humanos de Enfermagem no Hospital/tendências
20.
Neural Netw ; 125: 131-141, 2020 May.
Artigo em Inglês | MEDLINE | ID: mdl-32088567

RESUMO

In recent years, deep learning achieves remarkable results in the field of artificial intelligence. However, the training process of deep neural networks may cause the leakage of individual privacy. Given the model and some background information of the target individual, the adversary can maliciously infer the sensitive feature of the target individual. Therefore, it is imperative to preserve the sensitive information in the training data. Differential privacy is a state-of-the-art paradigm for providing the privacy guarantee of datasets, which protects the private and sensitive information from the attack of adversaries significantly. However, the existing privacy-preserving models based on differential privacy are less than satisfactory since traditional approaches always inject the same amount of noise into parameters to preserve the sensitive information, which may impact the trade-off between the model utility and the privacy guarantee of training data. In this paper, we present a general differentially private deep neural networks learning framework based on relevance analysis, which aims to bridge the gap between private and non-private models while providing an effective privacy guarantee of sensitive information. The proposed model perturbs gradients according to the relevance between neurons in different layers and the model output. Specifically, during the process of backward propagation, more noise is added to gradients of neurons that have less relevance to the model output, and vice-versa. Experiments on five real datasets demonstrate that our mechanism not only bridges the gap between private and non-private models, but also prevents the disclosure of sensitive information effectively.


Assuntos
Aprendizado Profundo , Redes Neurais de Computação , Privacidade , Inteligência Artificial/tendências , Aprendizado Profundo/tendências , Humanos
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