Featured Publications
Artificial intelligence-guided detection of under-recognised cardiomyopathies on point-of-care cardiac ultrasonography: a multicentre study
Oikonomou E, Vaid A, Holste G, Coppi A, McNamara R, Baloescu C, Krumholz H, Wang Z, Apakama D, Nadkarni G, Khera R. Artificial intelligence-guided detection of under-recognised cardiomyopathies on point-of-care cardiac ultrasonography: a multicentre study. The Lancet Digital Health 2025, 7: e113-e123. PMID: 39890242, DOI: 10.1016/s2589-7500(24)00249-8.Peer-Reviewed Original ResearchConceptsYale New Haven Health SystemPoint-of-care ultrasonographyMount Sinai Health SystemTransthyretin amyloid cardiomyopathyArtificial intelligenceHealth systemAmyloid cardiomyopathyHypertrophic cardiomyopathyRetrospective cohort of individualsCardiomyopathy casesTesting artificial intelligenceConvolutional neural networkSinai Health SystemCohort of individualsOpportunistic screeningHypertrophic cardiomyopathy casesMulti-labelPositive screenAI frameworkEmergency departmentMortality riskNeural networkLoss functionCardiac ultrasonographyAugmentation approachToward Realizing the Promise of AI in Precision Health Across the Spectrum of Care
Wiens J, Spector-Bagdady K, Mukherjee B. Toward Realizing the Promise of AI in Precision Health Across the Spectrum of Care. Annual Review Of Genomics And Human Genetics 2024, 25: 141-159. PMID: 38724019, DOI: 10.1146/annurev-genom-010323-010230.Peer-Reviewed Original ResearchChronic care managementSpectrum of careArtificial intelligenceClinical care decisionsAcademic medical centerEthical challengesClinical decision-makingImprove careCare decisionsPreventive careCare managementPrecision healthTertiary careLeveraging patient dataReduce inequalitiesCareMedical CenterInconsistent useSelection biasAI solutionsPatient dataMissing dataDecision-makingDesign imperfectionsArtificial intelligence-enhanced patient evaluation: bridging art and science
Oikonomou E, Khera R. Artificial intelligence-enhanced patient evaluation: bridging art and science. European Heart Journal 2024, 45: 3204-3218. PMID: 38976371, PMCID: PMC11400875, DOI: 10.1093/eurheartj/ehae415.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsTransforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice JACC State-of-the-Art Review
Khera R, Oikonomou E, Nadkarni G, Morley J, Wiens J, Butte A, Topol E. Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice JACC State-of-the-Art Review. Journal Of The American College Of Cardiology 2024, 84: 97-114. PMID: 38925729, DOI: 10.1016/j.jacc.2024.05.003.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsThe artificial intelligence-based model ANORAK improves histopathological grading of lung adenocarcinoma
Pan X, AbdulJabbar K, Coelho-Lima J, Grapa A, Zhang H, Cheung A, Baena J, Karasaki T, Wilson C, Sereno M, Veeriah S, Aitken S, Hackshaw A, Nicholson A, Jamal-Hanjani M, Swanton C, Yuan Y, Le Quesne J, Moore D. The artificial intelligence-based model ANORAK improves histopathological grading of lung adenocarcinoma. Nature Cancer 2024, 5: 347-363. PMID: 38200244, PMCID: PMC10899116, DOI: 10.1038/s43018-023-00694-w.Peer-Reviewed Original ResearchDetection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices
Khunte A, Sangha V, Oikonomou E, Dhingra L, Aminorroaya A, Mortazavi B, Coppi A, Brandt C, Krumholz H, Khera R. Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices. Npj Digital Medicine 2023, 6: 124. PMID: 37433874, PMCID: PMC10336107, DOI: 10.1038/s41746-023-00869-w.Peer-Reviewed Original ResearchArtificial intelligenceRandom Gaussian noiseNoisy electrocardiogramGaussian noiseElectrocardiogram (ECGWearable devicesSingle-lead electrocardiogramPortable devicesSNRWearableNoiseDevice noiseRepositoryAI-based screeningIntelligenceDetectionDevicesNoise sourcesVentricular systolic dysfunctionModelElectrocardiogramSingle-lead electrocardiographyTrainingCurrent trends in artificial intelligence in reproductive endocrinology
Bhaskar D, Chang TA, Wang S. Current trends in artificial intelligence in reproductive endocrinology. Current Opinion In Obstetrics & Gynecology 2022, 34: 159-163. PMID: 35895955, DOI: 10.1097/gco.0000000000000796.Peer-Reviewed Original ResearchUse of artificial intelligence in emergency radiology: An overview of current applications, challenges, and opportunities
Al-Dasuqi K, Johnson MH, Cavallo JJ. Use of artificial intelligence in emergency radiology: An overview of current applications, challenges, and opportunities. Clinical Imaging 2022, 89: 61-67. PMID: 35716432, DOI: 10.1016/j.clinimag.2022.05.010.Peer-Reviewed Original ResearchArtificial Intelligence in Stroke
Mishra N, Liebeskind D. Artificial Intelligence in Stroke. 2021, 1-19. DOI: 10.1007/978-3-030-58080-3_197-1.Peer-Reviewed Original Research
2025
Artificial Intelligence for Future Presidents: Teaching AI Literacy to Everyone
Candon K, Georgiou N, Ramnauth R, Cheung J, Fincke E, Scassellati B. Artificial Intelligence for Future Presidents: Teaching AI Literacy to Everyone. Proceedings Of The AAAI Conference On Artificial Intelligence 2025, 39: 28988-28995. DOI: 10.1609/aaai.v39i28.35168.Peer-Reviewed Original ResearchHarnessing Artificial Intelligence for Innovation in Interventional Cardiovascular Care
Aminorroaya A, Biswas D, Pedroso A, Khera R. Harnessing Artificial Intelligence for Innovation in Interventional Cardiovascular Care. Journal Of The Society For Cardiovascular Angiography & Interventions 2025, 4: 102562. PMID: 40230673, PMCID: PMC11993883, DOI: 10.1016/j.jscai.2025.102562.Peer-Reviewed Original ResearchClinical careCommunity-based screening programCare quality outcomesPatient outcomesPatient-focused careHarness artificial intelligenceArtificial intelligencePotential of AIImprove patient outcomesIndividualized clinical careTransform careTransform clinical practiceCardiovascular careScreening programHealth dataQuality outcomesCareClinical workflowClinical tasksAcute coronary syndromeClinical practiceHeart diseaseAI-driven technologiesInterventionAI-enabledGenerative artificial intelligence and machine learning methods to screen social media content.
Sharp K, Ouellette R, Singh R, DeVito E, Kamdar N, de la Noval A, Murthy D, Kong G. Generative artificial intelligence and machine learning methods to screen social media content. PeerJ Computer Science 2025, 11: e2710. PMID: 40134877, PMCID: PMC11935761, DOI: 10.7717/peerj-cs.2710.Peer-Reviewed Original ResearchSocial media contentMachine learning methodsGenerative artificial intelligenceHuman reviewScreen contentLearning methodsArtificial intelligenceHuman codersMedia contentMachine learning techniquesSocial media dataObject detectionOpenCV libraryCloud VisionText dataMetadata entriesExtract framesChatGPTLearning techniquesIrrelevant contentSocial media platformsSocial media researchIrrelevant resultsMedia dataRelevant contentArtificial 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.Facial Determinants of Artificial Intelligence–Perceived Gender and Age Following Facial Feminization Surgery
Vassallo M, Ihnat J, Flores-Pérez P, Rancu A, Allam O, Alperovich M. Facial Determinants of Artificial Intelligence–Perceived Gender and Age Following Facial Feminization Surgery. Journal Of Craniofacial Surgery 2025, 36: 734-738. DOI: 10.1097/scs.0000000000011135.Peer-Reviewed Original ResearchFacial feminization surgeryFacial thirdFeminization surgeryUpper thirdPostoperative imagingPostoperative changesFacial recognition softwarePostoperative ageYounger cohortsArtificial intelligence evaluationArtificial intelligenceLower thirdCohortRecognition softwareIntelligent evaluationTransgender womenSurgeryAgeAge scoresScoresArtificial Intelligence Meets Holistic Review: Promises and Pitfalls of Automating the Medical Education Admissions Process.
Rosenthal J, Hafferty F, Kulasegaram K, Wendland C, Taylor J. Artificial Intelligence Meets Holistic Review: Promises and Pitfalls of Automating the Medical Education Admissions Process. Academic Medicine 2025 PMID: 39993258, DOI: 10.1097/acm.0000000000005964.Peer-Reviewed Original ResearchArtificial Intelligence-Enhanced Perfusion Scoring Improves the Diagnostic Accuracy of Myocardial Perfusion Imaging.
Miller R, Kavanagh P, Lemley M, Liang J, Sharir T, Einstein A, Fish M, Ruddy T, Kaufmann P, Sinusas A, Miller E, Bateman T, Dorbala S, Di Carli M, Hayes S, Friedman J, Berman D, Dey D, Slomka P. Artificial Intelligence-Enhanced Perfusion Scoring Improves the Diagnostic Accuracy of Myocardial Perfusion Imaging. Journal Of Nuclear Medicine 2025, jnumed.124.268079. PMID: 39978815, DOI: 10.2967/jnumed.124.268079.Peer-Reviewed Original ResearchObstructive coronary artery diseaseCoronary artery diseaseArea under the receiver operating characteristic curveMyocardial perfusion imagingPerfusion scoreDetection of obstructive coronary artery diseaseDiagnostic accuracy of myocardial perfusion imagingPerfusion imagingAccuracy of myocardial perfusion imagingInvasive coronary angiographyCohort of patientsHighest area under the receiver operating characteristic curveLeft main coronary arteryReceiver operating characteristic curveStress TPDDeep learningObstructive CADMedian ageCoronary angiographyArtificial intelligenceDiagnostic performanceDiagnostic accuracyArtery diseaseAI predictionsCoronary arteryEvaluation of navigation and robotic systems for percutaneous image-guided interventions: A novel metric for advanced imaging and artificial intelligence integration
Cornelis F, Filippiadis D, Wiggermann P, Solomon S, Madoff D, Milot L, Bodard S. Evaluation of navigation and robotic systems for percutaneous image-guided interventions: A novel metric for advanced imaging and artificial intelligence integration. Diagnostic And Interventional Imaging 2025 PMID: 39884887, DOI: 10.1016/j.diii.2025.01.004.Peer-Reviewed Original ResearchPercutaneous image-guided interventionsArtificial intelligenceRobotic systemImage-guided interventionsNovel metricAdvanced imagingIntegration of advanced imagingArtificial intelligence integrationLevel of automationEvaluation of navigationIntelligent integrationLevel of autonomySurgical robotRobotic devicesNavigationIntegration of imaging technologyNavigation systemNavigation devicesWeb of Science databasesCochrane LibraryIntelligencePRISMA guidelinesAutomationMetricsAggregate scoreLeveraging Artificial Intelligence in the Diagnosis and Management of Pulmonary Embolism
Mojibian H, Khosla A. Leveraging Artificial Intelligence in the Diagnosis and Management of Pulmonary Embolism. 2025, 1-15. DOI: 10.1007/978-3-030-70904-4_72-1.Peer-Reviewed Original ResearchIntegrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges
Ye Y, Pandey A, Bawden C, Sumsuzzman D, Rajput R, Shoukat A, Singer B, Moghadas S, Galvani A. Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges. Nature Communications 2025, 16: 581. PMID: 39794317, PMCID: PMC11724045, DOI: 10.1038/s41467-024-55461-x.Peer-Reviewed Original ResearchConceptsArtificial intelligenceEffective public health planningPublic health planningMechanistic modelSystematic search strategyCapabilities of artificial intelligenceData-mining capabilitiesReview of opportunitiesFusion of AIPotential of AIHealth plansSpectrum of infectious diseasesDisease dynamicsPreparing for The Silver Tsunami: The Potential for use of Big Data and Artificial Intelligence in Geriatric Anesthesia
Chu L, Kurup V. Preparing for The Silver Tsunami: The Potential for use of Big Data and Artificial Intelligence in Geriatric Anesthesia. Current Anesthesiology Reports 2025, 15: 17. DOI: 10.1007/s40140-024-00674-5.Peer-Reviewed Original ResearchBig data analyticsData analyticsMachine learningPotential of big data analyticsAdoption of MLIntegration of MLData privacyBig DataData scientistsData-driven insightsArtificial intelligenceGeriatric anesthesiaAlgorithmic biasModel interpretationML modelsPreoperative risk stratificationPredicting postoperative complicationsIntraoperative anesthesia managementPostoperative complicationsRespiratory complicationsRisk stratificationElderly patientsDataAdverse outcomesAnesthesia management
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