2025
Artificial Intelligence–Guided Lung Ultrasound by Nonexperts
Baloescu C, Bailitz J, Cheema B, Agarwala R, Jankowski M, Eke O, Liu R, Nomura J, Stolz L, Gargani L, Alkan E, Wellman T, Parajuli N, Marra A, Thomas Y, Patel D, Schraft E, O’Brien J, Moore C, Gottlieb M. Artificial Intelligence–Guided Lung Ultrasound by Nonexperts. JAMA Cardiology 2025, 10: 245-253. PMID: 39813064, PMCID: PMC11904735, DOI: 10.1001/jamacardio.2024.4991.Peer-Reviewed Original ResearchThis study shows AI helps non-experts create expert-quality lung ultrasound images, which may improve healthcare diagnostics access in underserved areas.Predicting conversion to psychosis using machine learning: response to Cannon
Smucny J, Cannon T, Bearden C, Addington J, Cadenhead K, Cornblatt B, Keshavan M, Mathalon D, Perkins D, Stone W, Walker E, Woods S, Davidson I, Carter C. Predicting conversion to psychosis using machine learning: response to Cannon. Frontiers In Psychiatry 2025, 15: 1520173. PMID: 39882161, PMCID: PMC11775650, DOI: 10.3389/fpsyt.2024.1520173.Peer-Reviewed Original ResearchMachine learning algorithmsMachine learning modelsLearning algorithmsConversion to psychosisMachine learningLearning modelsStandard machine learning algorithmsClinical high riskNAPLS-2Overall performanceNaive BayesModel generalizationClinical high-risk individualsPredicting conversion to psychosisTest setIndependent datasetsRandom forest methodDataset
2024
Artificial Intelligence in Diagnosing and Managing Vascular Surgery Patients: An Experimental Study Using the GPT-4 Model
Alexiou V, Sumpio B, Vassiliou A, Kakkos S, Geroulakos G. Artificial Intelligence in Diagnosing and Managing Vascular Surgery Patients: An Experimental Study Using the GPT-4 Model. Annals Of Vascular Surgery 2024, 111: 260-267. PMID: 39586530, DOI: 10.1016/j.avsg.2024.11.014.Peer-Reviewed Original ResearchNatural language processingAI modelsArtificial intelligenceMachine learning algorithmsLanguage modelLearning algorithmsVascular surgery patientsRelevant answersLanguage processingAI chatbotsIntroduction of artificial intelligenceStandalone solutionMedical classification systemsTest scenariosSurgery patientsMedical informationClinical scenariosComplex problemsIntelligenceScientific fieldsComplex clinical scenariosScenariosStatistically significant differenceClinically relevant answersPerformance variationUsing Voice Data to Facilitate Depression Risk Assessment in Primary Health Care
Goyal A, Man R, Lee R, Saha K, Altice F, Poellabauer C, Papakyriakopoulos O, Cheung L, De Choudhury M, Allagh K, Kumar N. Using Voice Data to Facilitate Depression Risk Assessment in Primary Health Care. 2024, 17-18. DOI: 10.1145/3630744.3658408.Peer-Reviewed Original ResearchDepression riskPrimary health careDepression risk assessmentLower-income patientsHealth careTreating depressionVoice dataDepressionK-nearest neighbor classifierMachine learning algorithmsCollected voice dataRiskNeighbor classifierLearning algorithmsStable internet connectionK-nearestInternet connectionTelehealthCareA roadmap to artificial intelligence (AI): Methods for designing and building AI ready data to promote fairness
Kidwai-Khan F, Wang R, Skanderson M, Brandt C, Fodeh S, Womack J. A roadmap to artificial intelligence (AI): Methods for designing and building AI ready data to promote fairness. Journal Of Biomedical Informatics 2024, 154: 104654. PMID: 38740316, PMCID: PMC11144439, DOI: 10.1016/j.jbi.2024.104654.Peer-Reviewed Original ResearchArtificial intelligenceMachine learningNatural language processing techniquesRaw dataLife cycle of dataLanguage processing techniquesInput dataApplication of artificial intelligenceArtificial intelligence processesMachine learning algorithmsTransform raw dataNatural language processing algorithmsArtificial intelligence methodsApplication of AILanguage processing algorithmsLearning algorithmsIntelligent processingError rateIntelligence methodsData governanceProcessing algorithmsData expertiseAlgorithmic biasElectronic health record dataData frameworksIn vivo neuropil density from anatomical MRI and machine learning
Akif A, Staib L, Herman P, Rothman D, Yu Y, Hyder F. In vivo neuropil density from anatomical MRI and machine learning. Cerebral Cortex 2024, 34: bhae200. PMID: 38771239, PMCID: PMC11107380, DOI: 10.1093/cercor/bhae200.Peer-Reviewed Original ResearchConceptsMagnetic resonance imagingSynaptic densityNeuropil densityCellular densityArtificial neural networkNeural networkPositron emission tomographyAnatomical magnetic resonance imagingHealthy subjectsSynaptic activityMRI scansMachine learning algorithmsBrain's energy budgetEmission tomographyIn vivo MRI scansResonance imagingTissue cellularityLearning algorithmsDiffusion magnetic resonance imagingMachine learningMicroscopic interpretationInterpretation of functional neuroimaging dataIndividual predictionsSubjectsMachine learning evaluation in the Global Event Processor FPGA for the ATLAS trigger upgrade
Jiang Z, Carlson B, Deiana A, Eastlack J, Hauck S, Hsu S, Narayan R, Parajuli S, Yin D, Zuo B. Machine learning evaluation in the Global Event Processor FPGA for the ATLAS trigger upgrade. Journal Of Instrumentation 2024, 19: p05031. DOI: 10.1088/1748-0221/19/05/p05031.Peer-Reviewed Original ResearchProcessing tasksMachine learningComplexity of algorithm designIndividuals process tasksSignal processing tasksVolume of dataReal-time processingMachine learning algorithmsMachine learning evaluationLearning algorithmsOverall latencyFiltering decisionsFiltering taskATLAS experimentAlgorithm designEvent processorProcessing platformHigh energy physics applicationsFPGALarge Hadron ColliderAlgorithmResource utilizationMachineTaskHadron ColliderMultimodal active subspace analysis for computing assessment oriented subspaces from neuroimaging data
Batta I, Abrol A, Calhoun V, Initiative A. Multimodal active subspace analysis for computing assessment oriented subspaces from neuroimaging data. Journal Of Neuroscience Methods 2024, 406: 110109. PMID: 38494061, PMCID: PMC11100582, DOI: 10.1016/j.jneumeth.2024.110109.Peer-Reviewed Original ResearchConceptsBrain subspacesStandard machine learning algorithmsHigh-dimensional neuroimaging dataTrain machine learning modelsUnsupervised decompositionMachine learning algorithmsMachine learning modelsFunctional sub-systemsSubspace analysisSubspace componentsLearning algorithmsSupervised approachBiological traitsLearning modelsSub-systemsAlzheimer's diseaseSubspaceComputational frameworkActive subspaceCross-validation procedureNeuroimaging dataAD-related brain regionsAutomated identificationPredictive performanceOrientation subspacesChapter 2 Data access, data bias, data equity
Shung D, Laine L. Chapter 2 Data access, data bias, data equity. 2024, 13-26. DOI: 10.1016/b978-0-323-95068-8.00002-9.Peer-Reviewed Original ResearchExpert-centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation.
Hoebel K, Bridge C, Ahmed S, Akintola O, Chung C, Huang R, Johnson J, Kim A, Ly K, Chang K, Patel J, Pinho M, Batchelor T, Rosen B, Gerstner E, Kalpathy-Cramer J. Expert-centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation. Radiology Artificial Intelligence 2024, 6: e220231. PMID: 38197800, PMCID: PMC10831514, DOI: 10.1148/ryai.220231.Peer-Reviewed Original ResearchConceptsBrain tumor segmentationDeep learning algorithmsSegmentation qualityLearning algorithmsTumor segmentationBrain tumor segmentation algorithmQuantitative quality metricsTumor segmentation algorithmClinical expert evaluationSegmentation performanceAlgorithm evaluationSegmentation algorithmQuality metricsDice scoreHausdorff distanceSegmentation casesAlgorithmExperimental resultsExpert evaluationQuality evaluationMetricsSurvey article
2023
Prediction of outpatient waiting time: using machine learning in a tertiary children’s hospital
Li X, Liu W, Kong W, Zhao W, Wang H, Tian D, Jiao J, Yu Z, Liu S. Prediction of outpatient waiting time: using machine learning in a tertiary children’s hospital. Translational Pediatrics 2023, 12: 2030043-2032043. PMID: 38130586, PMCID: PMC10730972, DOI: 10.21037/tp-23-58.Peer-Reviewed Original ResearchMachine learning algorithmsLearning algorithmsOutpatient waiting timesMachine learningPediatric hospitalEnhance healthcare servicesGBDT modelDepartment categoryPatient-centred careWaiting timeEffective hospital managementClassification methodWaiting time of patientsTertiary children's hospitalHealthcare servicesMedical appointmentsOptimization modelAlgorithmPredicted waiting timeHospital managementPatient anxietyMachineChildren's hospitalOutpatient clinicDay of admissionLearning Product Rankings Robust to Fake Users
Golrezaei N, Manshadi V, Schneider J, Sekar S. Learning Product Rankings Robust to Fake Users. Operations Research 2023, 71: 1171-1196. DOI: 10.1287/opre.2022.2380.Peer-Reviewed Original ResearchFake usersOnline learning algorithmLearning algorithmsProduct rankingDetect fake usersEfficient learning algorithmClick farmingImplementing multiple levelsMachine learning algorithmsE-commerce platformsFraudulent behaviorFraudulent usersSuboptimal rankingsUser feedbackCorrupted dataData analyticsFraudulent actorsE-commerceOptimal rankingOnline platformsUsersTD managementDisplay orderLearning methodologyAlgorithm
2022
Quorum-based model learning on a blockchain hierarchical clinical research network using smart contracts
Kuo T, Pham A. Quorum-based model learning on a blockchain hierarchical clinical research network using smart contracts. International Journal Of Medical Informatics 2022, 169: 104924. PMID: 36402113, PMCID: PMC9984225, DOI: 10.1016/j.ijmedinf.2022.104924.Peer-Reviewed Original ResearchConceptsSmart contractsPrivacy-preserving modelHierarchical learning algorithmBlockchain smart contractsProtect patient privacyLearning algorithmsNetwork of networksConsensus protocolModeling processPatient privacyQuorum mechanismHierarchical networkAvailability issuesNetworkPrediction correctnessIterative phasesPrediction modelPrivacyImmutabilityCapabilityHealthcare institutionsAlgorithmDatasetIterationLearning continuityMetamodeling for Policy Simulations with Multivariate Outcomes
Zhong H, Brandeau M, Yazdi G, Wang J, Nolen S, Hagan L, Thompson W, Assoumou S, Linas B, Salomon J. Metamodeling for Policy Simulations with Multivariate Outcomes. Medical Decision Making 2022, 42: 872-884. PMID: 35735216, PMCID: PMC9452454, DOI: 10.1177/0272989x221105079.Peer-Reviewed Original ResearchConceptsAlgorithm adaptation methodsBase learnersGaussian process regressionHyperparameter tuningRandom forestElastic netAdaptive methodVariable selectionMultiple correlated outputsRegression chainsLearning algorithmsNeural networkAlgorithm adaptationMetamodelModel interpretationHyperparametersProcess regressionPolicy simulationsPrediction timePolicy analysisAlgorithmMultivariate outcomesMultioutputIn-sample fitDecision analysisHow Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning?
Zhuang C, Xiang V, Bai Y, Jia X, Turk-Browne N, Norman K, DiCarlo J, Yamins D. How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning? Advances In Neural Information Processing Systems 2022, 35: 22628-22642. PMID: 38435074, PMCID: PMC10906807.Peer-Reviewed Original ResearchSelf-supervised algorithmLearning algorithmsReal-timeStreams of visual inputNeural network modelHuman learning abilitiesMoCo v2Catastrophic forgettingLearning benchmarksLearning capabilityVisual inputReal worldHuman learnersNetwork modelVisual knowledgeLeverage memoryPerformance of modelsAlgorithmHuman performanceBenchmarksNegative samplesContext-sensitiveLearning abilityLearningVision setting
2021
Learning Product Rankings Robust to Fake Users
Golrezaei N, Manshadi V, Schneider J, Sekar S. Learning Product Rankings Robust to Fake Users. 2021, 560-561. DOI: 10.1145/3465456.3467580.Peer-Reviewed Original ResearchFake usersLearning algorithmsSub-optimal rankingsEfficient learning algorithmNew learning algorithmsCustomer actionsImplementing multiple levelsFraudulent behaviorFraudulent usersPerformance guaranteesIncurring large costsOptimal rankingOnline platformsUsersPairwise relationshipsClick farmingAlgorithmRanking robustnessProduct rankingInformation environmentCross-learningEfficient convergencePlatformLearningLearning process
2018
Deep networks in identifying CT brain hemorrhage
Helwan A, El-Fakhri G, Sasani H, Uzun Ozsahin D. Deep networks in identifying CT brain hemorrhage. Journal Of Intelligent & Fuzzy Systems 2018, Preprint: 1-1. DOI: 10.3233/jifs-172261.Peer-Reviewed Original ResearchConvolutional neural networkStacked autoencoderDeep networksMedical image classificationDeep learning algorithmsMedical expert's experienceImage classificationTraining timeLearning algorithmsNeural networkAutoencoderExpert experienceBrain CT imagesCT imagesNetworkHigher accuracyLess errorAlgorithmImagesAccuracyErrorClassificationArtificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability
Tajmir S, Lee H, Shailam R, Gale H, Nguyen J, Westra S, Lim R, Yune S, Gee M, Do S. Artificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability. Skeletal Radiology 2018, 48: 275-283. PMID: 30069585, DOI: 10.1007/s00256-018-3033-2.Peer-Reviewed Original ResearchConceptsBone age assessmentAutomated artificial intelligenceAI assistanceBone age radiographsConvolutional neural networkDeep learning algorithmsRoot mean square errorMean square errorPediatric radiologistsUtilization of AILearning algorithmsNeural networkArtificial intelligenceIntraclass correlation coefficientImproved performancePooled cohortRadiologist interpretationImaging studiesInter-rater variationAccuracyMetabolic disordersIncreased accuracyRadiologistsAge accuracyMeasures of accuracy
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