Featured Publications
Detection 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 electrocardiographyTraining
2024
Reviewer Experience Detecting and Judging Human Versus Artificial Intelligence Content: The Stroke Journal Essay Contest
Silva G, Khera R, Schwamm L, Acampa M, Adelman E, Boltze J, Broderick J, Brodtmann A, Christensen H, Dalli L, Duncan K, Elgendy I, Ergul A, Goldstein L, Hinkle J, Johansen M, Jood K, Kasner S, Levine S, Li Z, Lip G, Marsh E, Muir K, Ospel J, Pera J, Quinn T, Räty S, Ranta A, Richards L, Romero J, Willey J, Hillis A, Veerbeek J. Reviewer Experience Detecting and Judging Human Versus Artificial Intelligence Content: The Stroke Journal Essay Contest. Stroke 2024, 55: 2573-2578. PMID: 39224979, PMCID: PMC11529699, DOI: 10.1161/strokeaha.124.045012.Peer-Reviewed Original ResearchConceptsArtificial intelligenceEditorial board membersAuthor typeTraditional peer reviewLanguage modelIntelligent contentAuthor attributionGeneral textAI expertiseHuman authorityImproved accuracyAuthor's identityAuthor's manuscriptScientific journalsEssay contestPeer reviewPerception of qualityAuthorshipNature of authorshipIntelligenceLLMScientific writingScientific essayEssay qualityEssayDesigning medical artificial intelligence systems for global use: focus on interoperability, scalability, and accessibility
Oikonomou E, Khera R. Designing medical artificial intelligence systems for global use: focus on interoperability, scalability, and accessibility. Hellenic Journal Of Cardiology 2024 PMID: 39025234, DOI: 10.1016/j.hjc.2024.07.003.Peer-Reviewed Original ResearchArtificial intelligenceMedical artificial intelligence systemsDesigning AI systemsMachine learning systemsArtificial intelligence systemsBenefits of AIIntelligent systemsAI systemsLearning systemEnd-usersData typesAI developmentInteroperabilityTemporal settingAccessScalabilityTreatment of cardiovascular diseasesIntelligenceSystemMachineQuality assuranceInternational cohortCardiovascular diseaseObstaclesArtificial 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 StatementsArtificial Intelligence for Cardiovascular Care—Part 1: Advances JACC Review Topic of the Week
Elias P, Jain S, Poterucha T, Randazzo M, Lopez Jimenez F, Khera R, Perez M, Ouyang D, Pirruccello J, Salerno M, Einstein A, Avram R, Tison G, Nadkarni G, Natarajan V, Pierson E, Beecy A, Kumaraiah D, Haggerty C, Avari Silva J, Maddox T. Artificial Intelligence for Cardiovascular Care—Part 1: Advances JACC Review Topic of the Week. Journal Of The American College Of Cardiology 2024, 83: 2472-2486. PMID: 38593946, DOI: 10.1016/j.jacc.2024.03.400.Peer-Reviewed Original ResearchEnhanced image qualityHuman expertsLeverage AIEvaluation benchmarkArtificial intelligenceAI modelsAI advancementsDetect diseaseTraining methodsImage qualityReduced ejection fractionEvolving technologyValvular heart diseaseReal-world efficacyEjection fractionProvider experienceHeart diseaseTechnologyCardiovascular carePatient careUnique characteristicsIntelligenceBenchmarksArtificial Intelligence in Cardiovascular Care—Part 2: Applications JACC Review Topic of the Week
Jain S, Elias P, Poterucha T, Randazzo M, Lopez Jimenez F, Khera R, Perez M, Ouyang D, Pirruccello J, Salerno M, Einstein A, Avram R, Tison G, Nadkarni G, Natarajan V, Pierson E, Beecy A, Kumaraiah D, Haggerty C, Avari Silva J, Maddox T. Artificial Intelligence in Cardiovascular Care—Part 2: Applications JACC Review Topic of the Week. Journal Of The American College Of Cardiology 2024, 83: 2487-2496. PMID: 38593945, DOI: 10.1016/j.jacc.2024.03.401.Peer-Reviewed Original ResearchEVIDENCE FROM RANDOMIZED CONTROLLED TRIAL TO REAL-WORLD PATIENTS USING ELECTRONIC HEALTH RECORD-ADAPTED DIGITAL TWINS: A NOVEL APPLICATION OF GENERATIVE ARTIFICIAL INTELLIGENCE
Thangaraj P, Shankar S, Oikonomou E, Khera R. EVIDENCE FROM RANDOMIZED CONTROLLED TRIAL TO REAL-WORLD PATIENTS USING ELECTRONIC HEALTH RECORD-ADAPTED DIGITAL TWINS: A NOVEL APPLICATION OF GENERATIVE ARTIFICIAL INTELLIGENCE. Journal Of The American College Of Cardiology 2024, 83: 2340. DOI: 10.1016/s0735-1097(24)04330-4.Peer-Reviewed Original Research
2023
Machine learning in precision diabetes care and cardiovascular risk prediction
Oikonomou E, Khera R. Machine learning in precision diabetes care and cardiovascular risk prediction. Cardiovascular Diabetology 2023, 22: 259. PMID: 37749579, PMCID: PMC10521578, DOI: 10.1186/s12933-023-01985-3.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsArtificial intelligence solutionsArtificial intelligence productsData-driven methodIntelligence solutionsArtificial intelligenceMachine learningPersonalized solutionsIntelligence productsBias mitigationMachineKey issuesPredictive modelSuch modelsSuccessful applicationRisk predictionParadigm shiftIntelligenceKey propertiesApplicationsLearningPersonalized careFrameworkSolutionCurrent regulatory frameworkHealthcare
2022
Automated multilabel diagnosis on electrocardiographic images and signals
Sangha V, Mortazavi BJ, Haimovich AD, Ribeiro AH, Brandt CA, Jacoby DL, Schulz WL, Krumholz HM, Ribeiro ALP, Khera R. Automated multilabel diagnosis on electrocardiographic images and signals. Nature Communications 2022, 13: 1583. PMID: 35332137, PMCID: PMC8948243, DOI: 10.1038/s41467-022-29153-3.Peer-Reviewed Original ResearchConceptsConvolutional neural networkArtificial intelligenceApplication of AISignal-based dataSignal-based modelElectrocardiographic imagesECG imagesGrad-CAMImage-based modelsNeural networkDiagnosis modelECG signalsImagesClinical labelsValidation setLabelsExternal validation setMultilabelIntelligenceNetworkApplicationsModelBroad useSetBroader setting