ライフサイエンスコーパス: AI

1 AI can be used to identify anatomy within the surgical f

2 AI guidelines are based on data obtained with old-genera

3 AI has proved to be capable of completing a spectrum of

4 AI in general and computer vision in specific are emergi

5 AI is becoming ubiquitous, revolutionizing many aspects

6 AI models were trained on 2627 random frames from 290 LC

7 AI predictions were evaluated using 10-fold cross-valida

8 AI versus surgeon annotation of CVS components and intra

9 AI was found to be a function of mean rainfall: more pos

10 AI-annotated intraoperative events were associated with

11 AI-assisted analysis of lung involvement on submillisiev

12 AI-based biomarker monitoring may pave the way into the

13 AI-based CT FFR from triple-rule-out CT angiography data

14 AI-driven health interventions fit into four categories

15 AI-surgeon agreement for all CVS components exceeded 75%

16 AIs of Abeta PET were analyzed in correlation with TSPO

17 f the quorum sensing molecule autoinducer-2 (AI-2).

18 This is a retrospective review of 2219 AIs that were either surgically removed or nonoperativel

19 fundamental questions have been raised about AI-driven health interventions, and whether the tools, m

20 Accordingly, AI could have particularly transformative applications i

21 An AI is a powerful tool to assist physicians in the diagno

22 An AI system can be trained to detect and grade cancer in p

23 Oleic acid, an AI-2 inhibitor, exhibited antidepressant properties, red

24 e present an adaptive dialogue algorithm (an AI-enabled dialogue agent) to identify sequences of ques

25 SP6 has also been implicated as an AI candidate gene through its study in rodent models.

26 Establishment of an AI model often demands big datasets and an ability to ha

27 men with early-stage breast cancer taking an AI for > 30 days with a planned duration of >= 36 months

28 d (DCE) breast MRI is improved when using an AI system compared with conventionally available softwar

29 However, when an AI system recommends nonstandard care, there is no simil

30 l protocols evaluating interventions with an AI component.

31 ical trials evaluating interventions with an AI component.

32 e results on a map, which we refer to as an "AI-enabled glaucoma dashboard." We used density-based cl

33 pproach and administration of oleic acid and AI-2 were used to determine the effects of the microbiom

34 ion, among which aromaticity indices (AI and AI(mod)) are widely used.

35 d noncoronary cusp fusion, increasing AS and AI, and older age were independently associated with asc

36 essed for BAV morphology, severity of AS and AI, history of coarctation, and aortic dimensions.

37 patients with BAV, valve morphology, AS, and AI are independently associated with ascending aorta dil

38 nsights into migratory waterfowl ecology and AI disease dynamics that aid in better preparing for fut

39 AI in the mobile technology environment, and AI-based support had utility in simulations of second op

40 Conclusion: Simultaneous HDACIs and AI dosing in patients with cancer resistant to AI alone

41 ombination of quantitative phase imaging and AI, which provides information about unlabeled live cell

42 tegy of TENG for the application in IoTs and AI as energy supply or self-powered sensor, but also pre

43 Both NETD and AI-NETD afforded complete sequence coverage of these mol

44 standard ETD with a short reaction time and AI-ETD with a long reaction time.

45 ate that a single intravenous bolus of n-apo AI (CSL111, 80 mg/kg) delivered immediately after reperf

46 that intravenous infusion of the same n-apo AI (CSL111, 80 mg/kg) similarly reduced the level of cir

47 We further demonstrate that n-apo AI binds to neutrophils and monocytes, with preferential

48 A single intravenous bolus of n-apo AI delivered immediately post-myocardial infarction redu

49 N-apo AI reduced the cardiac expression of chemokines that att

50 preserved levels of HDL-C and apolipoprotein-AI and increased survival relative to placebo treatment

51 ostate cancer cells in WT and apolipoprotein-AI KO (apoA1-KO) C57BL/6J mice revealed that WT hosts, c

52 OI) from raw input MRI sequences by applying AI algorithms including a blend of Convolutional Neural

53 he adjacent dorsal intermediate arcopallium (AId), an avian analog of mammalian deep cortical layers

54 ity; autonomous AI systems in healthcare are AI systems that make clinical decisions without human ov

55 nterdisciplinary partnerships centred around AI applications towards SDGs.

56 niridic and aphakic eyes for ArtificialIris (AI) and IOL reconstruction.

57 romote depressive-like behaviors by AI-2, as AI-2 administration did not promote susceptibility to de

58 We assessed AI accuracy in evaluating the critical view of safety (C

59 L into the visual, but not into the auditory AI, revealed a massive projection to tectal layer 13 and

60 ons of high cognitive complexity; autonomous AI systems in healthcare are AI systems that make clinic

61 well as the potential benefits of autonomous AI for their patients.

62 e potential risks and benefits of autonomous AI, and understand its design, safety, efficacy and equi

63 The autonomous AI evaluation rules introduced here can help physicians

64 achievements in the accuracy of image-based AI for skin cancer diagnosis to address the effects of v

65 harma, TLC Biopharmaceuticals and Benevolent AI, has consulted with Lansdowne partners, Vitruvian and

66 ent prognostic value and interaction between AI-ECG AF model output and CHARGE-AF score.

67 There was a significant correlation between AIs of Abeta PET and TSPO PET in 4 investigated Abeta mo

68 pic cholecystectomy videos were annotated by AI for disease severity (Parkland Scale), CVS achievemen

69 ired to promote depressive-like behaviors by AI-2, as AI-2 administration did not promote susceptibil

70 gents, instead of being designed manually by AI researchers, might learn portions of their own knowle

71 is sufficient to affect amelogenesis causing AI, but not so severe as to be incompatible with life.

72 Class AI is topologically trivial in one to three spatial dime

73 model output, CHARGE-AF score, and combined AI-ECG and CHARGE-AF score.

74 We conclude AI-ETD has the potential to rapidly and comprehensively

75 CONSORT-AI recommends that investigators provide clear descripti

76 CONSORT-AI will help promote transparency and completeness in re

77 The CONSORT-AI (Consolidated Standards of Reporting Trials-Artificia

78 The CONSORT-AI extension includes 14 new items that were considered

79 and requirements for implementing continuous AI in radiology and illustrate them with examples from e

80 We suggest that current AI cohorts, both with autosomal recessive and dominant d

81 ion negative electron transfer dissociation (AI-NETD) to nucleic acids.

82 oves diagnostic accuracy over that of either AI or physicians alone, and that the least experienced c

83 mograms to gauge the performance of emerging AI CAD systems.

84 ccelerate the adaptation process of external AI models in hospitals.

85 Extracellular AI-2 detection was decreased in DeltaCjNC110; however, i

86 f non-expert clinicians, we find that faulty AI can mislead the entire spectrum of clinicians, includ

87 was 0.27 (95% CI = 0.23-0.31), also favoring AI (p < 0.001).

88 0.83 (95% CI = 0.81-0.85) for RMR, favoring AI (p < 0.001).

89 ch-top dehydration (BD), air-injection-flow (AI), pneumatic-air-discharge (PAD), optical (OP) and X-r

90 The AUC was 0.86 (95% CI = 0.85-0.88) for AI and 0.83 (95% CI = 0.81-0.85) for RMR, favoring AI (p

91 C statistics were calculated for AI-ECG AF model output, CHARGE-AF score, and combined AI

92 t were considered sufficiently important for AI interventions that they should be routinely reported

93 ompleteness for clinical trial protocols for AI interventions.

94 -AID's three-pronged integrated strategy for AI adoption in resource-poor health institutions is pres

95 ompleteness in reporting clinical trials for AI interventions.

96 pilot resulted in establishment of a formal AI-ML curriculum for future residents.

97 Boron-free AI-2 is the preferred ligand for PctA and TlpQ.

98 Lastly, we show that insights derived from AI class-activation maps can inform improvements in huma

99 st experienced clinicians gain the most from AI-based support.

100 AUC values from AI-assisted analysis were significantly higher than thos

101 handling of input and output data, the human-AI interaction and analysis of error cases.

102 nd outputs of the AI intervention, the human-AI interaction and provision of an analysis of error cas

103 A hybrid AI system was developed that simultaneously provides a q

104 Amelogenesis imperfecta (AI) is a collection of genetic disorders affecting the q

105 Amelogenesis imperfecta (AI) is a heterogeneous group of genetic conditions that

106 Most importantly, AI-ETD reveals disulfide-bound regions that have been in

107 Continued refinement may improve AI applicability and allow for automated assessment.

108 ial of an intervention directed at improving AI adherence.

109 The optimal size cut-off for ACC in AI was 4.6 cm.

110 This review addresses the challenges in AI development, deployment, and regulation to be overcom

111 steps for preparing medical imaging data in AI algorithm development, explain current limitations to

112 Other findings found in AI: diverticula (30), accessory auricular appendages (5)

113 Overall, ACC incidence in AI was 1.7%.

114 orians, and diagnosticians are interested in AI-based testing because these solutions have the potent

115 To develop as leaders in AI-ML, radiology residents may seek a formative data sci

116 ealth institutions' limited participation in AI production and validation.

117 We highlight recent progress in AI toward answering these questions in the domain of vis

118 lysis demonstrates that ACC risk per size in AI is less than previously reported.

119 g the power of Big data analytics (including AI) with existing and future urban water infrastructure

120 re enables radiology workflows incorporating AI.

121 Indeed, AI has the potential to improve the accuracy, precision,

122 The measurement of the acetabular index (AI) on plain pelvis X-rays was used to identify persiste

123 The asymmetry index (AI) was calculated between tracer uptake in both hemisph

124 We used an asymmetry index (AI) where positive AI indicates a greater increase of ec

125 ttribution, among which aromaticity indices (AI and AI(mod)) are widely used.

126 ut later progression on aromatase inhibitor (AI) therapy were given vorinostat (400 mg daily) sequent

127 ) resistant to adjuvant aromatase inhibitor (AI) therapy.

128 Nonadherence to aromatase inhibitors (AIs) for breast cancer is common and increases the risk

129 d to men who exclusively practiced insertive AI.

130 tic stenosis (AS), and aortic insufficiency (AI) have been proposed as potential risk factors; howeve

131 The hub role of the right anterior insula (AI) has been emphasized in cognitive neurosciences and b

132 An artificial intelligence (AI) algorithm applied to electrocardiography during sinu

133 The artificial intelligence (AI) algorithm presented here has a precision which is su

134 ss has been made in artificial intelligence (AI) and its application to medicine.

135 The emergence of artificial intelligence (AI) and its progressively wider impact on many sectors r

136 Artificial intelligence (AI) and machine learning (ML) in medicine are currently

137 ern technologies of artificial intelligence (AI) and microfluidics.

138 the development of artificial intelligence (AI) applications.

139 propose the use of artificial intelligence (AI) approaches to build a data-driven framework that int

140 based biosensor and artificial intelligence (AI) assisted diagnosis of COVID-19 are emphasized.

141 has been shown that artificial intelligence (AI) can transform one form of contrast into another.

142 Artificial intelligence (AI) describes systems capable of making decisions of hig

143 ance on cloud-based artificial intelligence (AI) for data analysis; (5) potential bias of interpretiv

144 Artificial intelligence (AI) has demonstrated promise in predicting acute kidney

145 Artificial intelligence (AI) has numerous applications in surgical quality assura

146 Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT s

147 Artificial intelligence (AI) has the potential to fundamentally alter the way med

148 est developments in artificial intelligence (AI) have arrived into an existing state of creative tens

149 Although artificial intelligence (AI) helps to reduce the labor of reading pathologic slid

150 Breakthroughs in artificial intelligence (AI) hold enormous potential as it can automate complex t

151 Artificial intelligence (AI) holds promise for cardiovascular medicine but is lim

152 Artificial intelligence (AI) is becoming established in drug discovery.

153 As artificial intelligence (AI) is increasingly applied to biomedical research and c

154 Advanced artificial intelligence (AI) methods in high-resolution retinal imaging allows to

155 e raised hopes that artificial intelligence (AI) might help to address challenges unique to the field

156 rventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to

157 rventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to

158 e learning (ML) and artificial intelligence (AI) present an opportunity to build better tools and sol

159 An artificial intelligence (AI) program, consisting of recurrent networks followed b

160 Background Although artificial intelligence (AI) shows promise across many aspects of radiology, the

161 Here we present an artificial intelligence (AI) system that is capable of surpassing human experts i

162 ye, we introduce an artificial intelligence (AI) system to predict progression to exAMD in the second

163 performance of the artificial intelligence (AI) system was assessed by comparing diagnostic accuracy

164 he radiologists and artificial intelligence (AI) system were calculated on a subset of 100 random int

165 As artificial intelligence (AI) systems begin to make their way into clinical radiol

166 Artificial intelligence (AI) systems for computer-aided diagnosis and image-based

167 er of automated and artificial intelligence (AI) systems make medical treatment recommendations, incl

168 f Things (IoTs) and artificial intelligence (AI).

169 the perspective of artificial intelligence (AI): If you have an intelligent agent that uses visual i

170 en who practiced receptive anal intercourse (AI) were more likely to present with secondary syphilis,

171 region of the dorsal (AD) and intermediate (AI) arcopallium, in between previously described auditor

172 ehensively track the assembly intermediates (AIs) of complex I (CI) biogenesis in Drosophila will ena

173 residents were successfully integrated into AI-ML projects at CCDS.

174 ased in DeltaCjNC110; however, intracellular AI-2 accumulation was significantly increased, suggestin

175 Continuous learning AI presents the next substantial step in this direction

176 ose To present DeepCOVID-XR, a deep learning AI algorithm to detect COVID-19 on chest radiographs, th

177 However, the functional organization of left AI (LAI) has not been systematically investigated.

178 Low pathogenic (Low-path LP) AI in chickens caused by less virulent strains of AI vir

179 Toward this goal, approaches to make AI "interpretable" have gained attention to enhance the

180 Despite the field remaining nascent, AI-driven health interventions could lead to improved he

181 ed to the decline in dry years, and negative AI indicates a greater decline of ecosystem productivity

182 antage of rainfall pulses, and more negative AIs were found in wet areas, with a threshold delineatin

183 retation, we show that our trained network ("AI-TAC") does so by rediscovering ab initio the binding

184 insufficient data diversity, nontransparent AI algorithms, and resource-poor health institutions' li

185 This has led to wide-spread adoption of AI-powered tools, in pursuit of improving accuracy and e

186 Here, we present the first application of AI-ETD to mAb sequencing.

187 rogress and potential dental applications of AI in medical-aided diagnosis, treatment, and disease pr

188 dies will be required before the approval of AI techniques by the health authorities.

189 hat SP6 variants may be a very rare cause of AI due to the critical roles of SP6 in development and t

190 still beyond reach, the virtual component of AI, known as software-type algorithms, is the main compo

191 terventions that led to changes in degree of AI and AS did not seem to influence change in aortic dim

192 Deployment of AI has already begun for a broad range of health issues

193 iate validation, adoption, and deployment of AI technologies into clinical practice.

194 e, we first provide a general description of AI methods, followed by a high-level overview of the rad

195 glect of ethical principles in the design of AI frameworks.

196 logy that are amenable to the development of AI diagnostics include genomic information from isolated

197 istry, * ca. 2020?" Then move to examples of AI affecting social matters, ranging from trivial to sca

198 thout AI and the other half with the help of AI during a first session and vice versa during a second

199 ithms that prevent routine implementation of AI include the lack of data curation, sharing, and reada

200 Proper integration of AI-based systems into anatomic-pathology practice will n

201 ns tailored to the nature and limitations of AI are currently in development and, when instituted, ar

202 McKinney et al. showed the high potential of AI for breast cancer screening.

203 We found high rates of AI AF.

204 ed image retrieval (CBIR) representations of AI in the mobile technology environment, and AI-based su

205 ess the effects of varied representations of AI-based support across different levels of clinical exp

206 chickens caused by less virulent strains of AI viruses (AIVs)-when compared with highly pathogenic A

207 le of CjNC110 in modulating the transport of AI-2.

208 a method to predict the transportability of AI models which can accelerate the adaptation process of

209 The trustworthiness of AI for medical decision making in global health and low-

210 Literacy and understanding of AI/ML methods are becoming increasingly important to res

211 across many aspects of radiology, the use of AI to create differential diagnoses for rare and common

212 ime was on average increased with the use of AI.

213 16.8% of AIs underwent upfront surgery and rest initial nonoperat

214 T were analyzed in correlation with TSPO PET AIs.

215 n, infrastructure implementation, and phased AI introduction.

216 used an asymmetry index (AI) where positive AI indicates a greater increase of ecosystem productivit

217 e a function of mean rainfall: more positive AIs were found in dry areas where plants are adapted to

218 clinical history are available, the proposed AI system can help to rapidly diagnose COVID-19 patients

219 ential benefits associated with good quality AI in the hands of non-expert clinicians, we find that f

220 We find that good quality AI-based support of clinical decision-making improves di

221 However, as radiology AI matures to become fully integrated into the daily rad

222 For the market overview, a list of radiology AI companies was aggregated from the Radiological Societ

223 Materials and Methods The CO-RADS AI system consists of three deep-learning algorithms tha

224 However, there is another area of recent AI work that has so far received less attention from neu

225 Hence, men who practice receptive AI may need additional strategies to detect anal chancre

226 ide preparation and image collection reduces AI model performance in cross-hospital tests, but the 10

227 SFB produces the quorum-sensing AI-2 and promotes the production of SAA1 and SAA2 by the

228 SPIRIT-AI recommends that investigators provide clear descripti

229 SPIRIT-AI will help promote transparency and completeness for c

230 The SPIRIT-AI (Standard Protocol Items: Recommendations for Interve

231 we evaluate the performance of a standalone AI tool to correctly categorize a skin lesion's morpholo

232 Current supervised AI methods require a curation process for data to optima

233 intelligence-based decision support system (AI-DSS) is as effective and safe as those guided by phys

234 reinforcements of the recommendation to take AIs.

235 Compared with tamoxifen, AIs were associated with an increased risk of myocardial

236 between these extremes, yet it is clear that AI is providing new challenges not only for the scientis

237 sed expert elicitation process, we find that AI can enable the accomplishment of 134 targets across a

238 We further find that AI-based multiclass probabilities outperformed content-b

239 We hypothesized that AI accuracy and intraoperative events are associated wit

240 Our results support the idea that AI-2 serves as a widely used signaling molecule in the c

241 low with discussion of the implications that AI is likely to have on each step of this process.

242 mmended clinical guidelines, suggesting that AI algorithms have the potential to provide a step chang

243 al TFs and providing additional support that AI-TAC is a generalizable regulatory sequence decoder.

244 The AI achieved an area under the receiver operating charact

245 The AI for Social Good (AI4SG) movement aims to establish in

246 The AI model was 93.9% sensitive (95% CI: 90.0, 96.7) and 97

247 The AI was subsequently tested on an additional set of 222 h

248 The AI-causing mutations in family 1 were a novel AMELX muta

249 The AI-ECG may offer a means to assess risk with a single te

250 The AI/ML algorithm helped predict AKI 61.8 (32.5) hours fas

251 e concordance between grades assigned by the AI system and the expert urological pathologists using C

252 pendently analyzed by six readers and by the AI system.

253 35=) and c.389C>T, p.(Thr130Ile)] caused the AI in family 2 and family 3.

254 acy of top three differential diagnoses, the AI system (91% correct) performed similarly to academic

255 -180 mg dl(-1) (3.9-10.0 mmol l(-1)))-in the AI-DSS arm were statistically non-inferior to those in t

256 eported in the physician arm and none in the AI-DSS arm.

257 e belonging to a subfamily that includes the AI-2 receptors identified in the present work) are prese

258 score (BSS) measured the improvement of the AI Brier score compared to the benchmark RMR Brier score

259 estigators provide clear descriptions of the AI intervention, including instructions and skills requi

260 estigators provide clear descriptions of the AI intervention, including instructions and skills requi

261 d, the handling of inputs and outputs of the AI intervention, the human-AI interaction and provision

262 This robust assessment of the AI system paves the way for clinical trials to improve t

263 The performance of the AI system was not affected by disease prevalence (93% ac

264 However, much of the AI-driven intervention research in global health does no

265 In a test set of 279 patients, the AI system achieved an area under the curve of 0.92 and h

266 zing system (MICOS), fully recapitulates the AI profile observed when AIF is inhibited.

267 test results as the reference standard, the AI system correctly classified chest radiographs as COVI

268 s that is used in the UK, and found that the AI system maintained non-inferior performance and reduce

269 Reading time changed dependently to the AI-tool score.

270 ed random (RMR) forecast was compared to the AI.

271 The scenarios varied the AI recommendation (standard or nonstandard care) and the

272 When the AI's top prediction was broadened to its top three most

273 s required for use, the setting in which the AI intervention is integrated, the handling of inputs an

274 s required for use, the setting in which the AI intervention will be integrated, considerations for t

275 We ran a simulation in which the AI system participated in the double-reading process tha

276 This AI system overcomes substantial interobserver variabilit

277 demonstrated that the concurrent use of this AI tool improved the diagnostic performance of radiologi

278 We propose to repurpose this AI to test ecological hypotheses that have been intracta

279 ons about new technologies can be applied to AI.

280 of the DSP was to provide an introduction to AI-ML through a flexible schedule of educational, experi

281 dosing in patients with cancer resistant to AI alone showed clinical benefit (6 or more months witho

282 text reminders did not improve adherence to AIs.

283 Overall, 1,962 patients switching to AIs were matched to 3,874 patients continuing tamoxifen.

284 ratio, patients switching from tamoxifen to AIs with patients continuing tamoxifen between 1998 and

285 ce in medical imaging datasets used to train AI systems for computer-assisted diagnosis.

286 Mouse-trained AI-TAC can parse human DNA, revealing a strikingly simil

287 and clinical decisions, developing unbiased AI models that work equally well for all ethnic groups i

288 We calculated the probability of AF using AI-ECG, among participants in the population-based Mayo

289 rts, and demonstrates the potential of using AI to predict disease progression.

290 images from three hospitals separately using AI models, and obtain a diagnostic rate of close to 100

291 Here, we utilized AI for monitoring the expression of underglycosylated mu

292 adely labeled neurons confined to the visual AI.

293 We review the ways in which AI may help physicians make a diagnosis or establish a p

294 ed yet or known how they contribute to wider AI and immune system issues.

295 We discuss integration of blockchain with AI for data-centric analysis and information flow, its c

296 Participants with AI-ECG AF model output of >0.5 at the baseline visit had

297 an alpha error of 0.05 was 250 patients with AI-assisted analysis and 1014 patients with visual analy

298 g daily) sequentially or simultaneously with AI.

299 sis that leverages our existing systems with AI, and the delivery of a recommendation.

300 n which half of the dataset was read without AI and the other half with the help of AI during a first