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1 s: Recommendations for Interventional Trials-Artificial Intelligence).
2 (Consolidated Standards of Reporting Trials-Artificial Intelligence).
3 se such technologies as machine learning and artificial intelligence.
4 arch, aptly referred to as the Drosophila of artificial intelligence.
5 ntially on how to design biological inspired artificial intelligence.
6 ense remains a major obstacle to progress in artificial intelligence.
7 obotics, sensing, personalized medicare, and artificial intelligence.
8 ticle' to include artificial neurons used in Artificial Intelligence.
9 ew directions for neuromorphic computing and artificial intelligence.
10 e press sensor, human-machine interface, and artificial intelligence.
11 come a powerful tool of machine learning and artificial intelligence.
12 candidate systems in the emerging fields of artificial intelligence.
13 ociated with learning continuous theories by artificial intelligence.
14 toelectronics, human-machine interfacing and artificial intelligence.
15 one of the main challenges in the pursuit of artificial intelligence.
16 lectronics are rapidly promoting advances in artificial intelligence.
17 e ill prepared to adopt machine learning and artificial intelligence.
19 hnological (eg, eHealth) and methodological (artificial intelligence) advances and their relevance fo
27 tion but is not well captured by widely used artificial intelligence (AI) and computational modeling
28 2010, substantial progress has been made in artificial intelligence (AI) and its application to medi
31 ilable data present an opportunity for using artificial intelligence (AI) and machine learning to imp
32 de images of tissue has led to the advent of artificial intelligence (AI) and machine learning tools
37 plasmon resonance (SPR)-based biosensor and artificial intelligence (AI) assisted diagnosis of COVID
42 e coupled with recent advances in diagnostic artificial intelligence (AI) create the imperative to co
44 pants; (4) extensive reliance on cloud-based artificial intelligence (AI) for data analysis; (5) pote
45 bsent radiology resources impede adoption of artificial intelligence (AI) for medical imaging by reso
55 an application of machine learning (ML) and artificial intelligence (AI) in medical imaging, promote
56 healthcare chain are looking to incorporate artificial intelligence (AI) into their decision-making
64 onential growth of computational algorithms, artificial intelligence (AI) methods are poised to impro
66 ome countries (LMICs) have raised hopes that artificial intelligence (AI) might help to address chall
67 ing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, p
68 ing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, p
75 Purpose To evaluate the performance of an artificial intelligence (AI) system for detection of COV
77 nosed with exAMD in one eye, we introduce an artificial intelligence (AI) system to predict progressi
80 coefficients (ICCs) for the radiologists and artificial intelligence (AI) system were calculated on a
81 viate these problems, we aimed to develop an artificial intelligence (AI) system with clinically acce
86 innovation and first steps using crowds and artificial intelligence (AI) to augment creativity that
87 tudy used personalised medicine approach and Artificial Intelligence (AI) to automatically detect noc
90 We tested the hypothesis that application of artificial intelligence (AI) to the electrocardiogram (E
91 s systematic experimental investigation with artificial intelligence (AI) tools allowed us to rapidly
94 and benign diseases, either visually or with artificial intelligence (AI), allows radiologists to imp
100 que widely used in various fields, including artificial intelligence (AI), signal processing and bioi
101 ecent years there have been great strides in artificial intelligence (AI), with games often serving a
102 fication methods and tools which incorporate artificial intelligence (AI)-associated data analysis pr
103 ground There is great interest in developing artificial intelligence (AI)-based computer-aided detect
107 The POTTER tool was derived using a novel Artificial Intelligence (AI)-methodology called optimal
111 was to assess the theoretical performance of artificial intelligence (AI)/machine learning (ML) algor
112 rcumscribed question from the perspective of artificial intelligence (AI): If you have an intelligent
113 tomated by using computer vision, an area of artificial intelligence aimed at interpreting images.
114 eveloped and validated a deep learning-based artificial intelligence algorithm (DLA) for detecting MI
121 ormance difference between radiologist's and artificial intelligence algorithm: artificial intelligen
122 gnificantly higher results compared with the artificial intelligence algorithm: artificial intelligen
124 theories of knowledge, "neat" approaches in artificial intelligence and decision theory, neo-empiric
126 machine learning have enabled the synergy of artificial intelligence and digital pathology, which off
128 rawing equally on cognitive neuroscience and artificial intelligence and exploiting query-based atten
129 approach to decision support that leverages artificial intelligence and game elements to restructure
133 es in the field of predictive modeling using artificial intelligence and machine learning have the po
138 -generation memory devices for employment in artificial intelligence and neuromorphic computing, due
140 tion of single-cell multi-omics and imaging, artificial intelligence and patient-derived experimental
143 sual memory are fundamental requirements for artificial intelligence and robotics with autonomous nav
144 veral speakers discussed the capabilities of artificial intelligence and the potential for use of mac
146 rovide to human users (so-called explainable artificial intelligence); and 5, validated methods for i
147 review recent work on optical computing for artificial intelligence applications and discuss its pro
148 cameras can provide in teleophthalmology and artificial intelligence applications, their use as black
150 is was performed automatically using a novel artificial intelligence approach deriving global and reg
151 this study was to evaluate a fully automated artificial intelligence approach for predicting the stat
153 natural language processing techniques as an artificial intelligence approach have been leveraged to
161 nical examples in which machine learning and artificial intelligence are already in use in health car
165 ist's and artificial intelligence algorithm: artificial intelligence-area under the receiver-operatin
166 with the artificial intelligence algorithm: artificial intelligence-area under the receiver-operatin
167 n imaging technologies, and the emergence of artificial intelligence as a diagnostic supplement.
168 en the algorithm and radiologists, we regard artificial intelligence as a promising clinical decision
169 lity and benefits of using histopathological artificial intelligence assistance systems in routine pr
170 s offers a unique opportunity for the use of artificial intelligence assistance systems to alleviate
176 n computerized image analysis and the use of artificial intelligence-based approaches for image-based
177 merging optical imaging modalities and novel artificial intelligence-based approaches, as well as to
178 a calibrated fluorescence assay (CF) with an artificial intelligence-based cell simulation to quantif
179 he current status and future perspectives of artificial intelligence-based clinical applications for
180 ulin dose adjustments guided by an automated artificial intelligence-based decision support system (A
181 ential for integration with rapidly emerging artificial intelligence-based decision-making strategies
183 can be a useful adjunct to human-focused and artificial intelligence-based forms of research in order
184 n are methodological issues in evaluation of artificial intelligence-based interventions, reporting s
190 alphaHDPs is inherently difficult, even for artificial-intelligence-based methods that seek multifac
192 and we believe there is clear potential for artificial intelligence breakthroughs in the pathology s
193 thod invokes recent advances in the field of artificial intelligence by utilizing a limited amount of
194 ce, and the ways in which it may differ from artificial intelligence, by considering the characterist
195 tate simulations but also how this branch of artificial intelligence can be used to advance this exci
197 ote (<1 mm), bringing efficient and low-cost artificial intelligence close to the end user and Intern
198 public under-reaction to the malfunctioning artificial intelligence components of automated cars and
199 ations in social networks, image processing, artificial intelligence, computational biology and a var
200 ned quantitative bright-field microscopy and artificial intelligence (deep neural networks and tradit
201 02 minutes; 95% CI: 11.06, 12.97; P < .0001) artificial intelligence-detected ICH examinations with r
202 DrBioRight, a natural language-oriented and artificial intelligence-driven analytics platform, enabl
203 ieves chemists from routine tasks, combining artificial intelligence-driven synthesis planning and a
204 onnect psychological theory more deeply with artificial intelligence, economics, neuroscience, and li
207 this study was to assess the accuracy of an artificial intelligence-enabled ECG to identify patients
208 (Consolidated Standards of Reporting Trials-Artificial Intelligence) extension is a new reporting gu
209 s: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting gu
210 ty of using pervasive sensing technology and artificial intelligence for autonomous and granular moni
213 multilayered) convolutional neural networks, artificial intelligence has undergone a transformation t
218 evel in a coauthor network from the field of artificial intelligence in education, an emerging interd
220 ively, our results highlight applications of artificial intelligence in molecular cancer pathology an
221 velopment of machine-learning techniques and artificial intelligence in particular has promised to re
223 ) methods have driven impressive advances in artificial intelligence in recent years, exceeding human
229 so highlight the potential and challenges of artificial intelligence, machine learning and deep learn
230 of individuals susceptible to nausea, using artificial intelligence/machine learning; brain data may
231 ical and more recently, machine learning and artificial intelligence methods in chemical sciences.
232 nts, improved data visualization, the use of artificial intelligence methods, and the implementation
233 it is difficult to develop a mathematical or artificial intelligence model that describes the time ev
235 ccess to health, researchers in the field of artificial intelligence must become actively anti-racist
238 ngoing developments, including the impact of artificial intelligence on the field of OA imaging, will
243 and MPR measured automatically inline using artificial intelligence quantification of cardiovascular
244 in silico, the efficacy of an approach from artificial intelligence-reinforcement learning-for the c
245 are approaches to biomimetic or neuromorphic artificial intelligence rely on elaborate transistor cir
246 tion error theory of dopamine, imported from artificial intelligence, represents the full distributio
248 Recent advances in machine learning and artificial intelligence research have opened up new ways
249 ed reinforcement learning inspired by recent artificial intelligence research on distributional reinf
250 yer games have long been used as testbeds in artificial intelligence research, aptly referred to as t
251 ft has emerged as an important challenge for artificial intelligence research, owing to its iconic an
253 nowledge of organic chemistry and data-based artificial intelligence routines are augmented with caus
254 that, at this exciting time for genomics and artificial intelligence, several critical aspects of dat
255 level the playing field between natural and artificial intelligence, so that we can separate more su
259 ancer detection at mammography when using an artificial intelligence system for support, without requ
262 ng of adult chest radiographs with use of an artificial intelligence system is feasible, with clinica
267 SBCE field of cutting-edge technologies, as artificial intelligence systems, is likely to shorten th
272 regards the prospects and pitfalls of using artificial intelligence techniques to automate an increa
273 canonical video game environment for testing artificial intelligence techniques, in which model-based
274 rithms, particularly with recent advances in artificial intelligence techniques, match human expert p
275 flexibility and the integration of powerful artificial intelligence technologies such as deep learni
276 tilize advanced spatial, location-aware, and artificial intelligence technologies to investigate long
277 l applications of deep learning, the leading artificial intelligence technology for image analysis, a
279 it is an insufficient guide for building an artificial intelligence that learns to accomplish short-
280 fficient biologically inspired algorithms in artificial intelligence, the human multiple-target track
282 This work illustrates the power of modern artificial intelligence to aid in discovery of accurate
283 eads from natural sciences, engineering, and artificial intelligence to contemporary classical and ro
285 astly, we will review the potential role for artificial intelligence to improve image analysis, disea
286 We combine drone-borne thermal imaging with artificial intelligence to locate ground-nests of birds
287 e this with machine learning techniques from artificial intelligence to select features relevant to r
288 ties for virtual clinical trials, the use of artificial intelligence to streamline and interpret data
289 ciated with condylar morphology and to apply artificial intelligence to test shape analysis features
290 rome, and we suggest that the application of artificial intelligence to the analysis of patient image
292 p learning, a recently dominant technique of artificial intelligence, to automatically extract implic
293 nic, racial, age, and sex groups for all new artificial intelligence tools to ensure responsible use
296 The technique of deep learning, an arm of artificial intelligence, using color fundus photographs
297 for machine intelligence-promises to realize artificial intelligence while reducing the energy requir
298 The last decade has transformed the field of artificial intelligence, with deep learning at the foref
299 hroughput computation, machine learning, and artificial intelligence work collectively to reveal the
300 ral network (DNN) and associated explainable artificial intelligence (XAI) algorithms, we show how ma