Featured Publications
Clinical Implementation of a Combined Artificial Intelligence and Natural Language Processing Quality Assurance Program for Pulmonary Nodule Detection in the Emergency Department Setting
Cavallo J, de Oliveira Santo I, Mezrich J, Forman H. Clinical Implementation of a Combined Artificial Intelligence and Natural Language Processing Quality Assurance Program for Pulmonary Nodule Detection in the Emergency Department Setting. Journal Of The American College Of Radiology 2023, 20: 438-445. PMID: 36736547, DOI: 10.1016/j.jacr.2022.12.016.Peer-Reviewed Original ResearchMeSH KeywordsArtificial IntelligenceEmergency Service, HospitalHumansLung NeoplasmsMultiple Pulmonary NodulesNatural Language ProcessingTomography, X-Ray ComputedConceptsEmergency department settingPulmonary nodulesCT examinationsDepartment settingSecondary reviewNumber of patientsQuality assurance studyMedian timeEmergent settingPatient followImaging recommendationsAppropriate followMajority of reviewsRadiological reportsAssurance studyClinical implementationLung anatomyQuality assurance programPatientsSignificant delayNodulesFollowExaminationPulmonary nodule detectionNodule detectionArtificial 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 approachTransforming 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 ResearchMeSH KeywordsAdenocarcinomaAdenocarcinoma of LungArtificial IntelligenceHumansLung NeoplasmsNeoplasm StagingChatGPT: Temptations of Progress
Doshi R, Bajaj S, Krumholz H. ChatGPT: Temptations of Progress. The American Journal Of Bioethics 2023, 23: 6-8. PMID: 36853242, DOI: 10.1080/15265161.2023.2180110.Peer-Reviewed Original ResearchArtificial IntelligenceHumansArtificial Intelligence in Breast Cancer Screening
Potnis K, Ross J, Aneja S, Gross C, Richman I. Artificial Intelligence in Breast Cancer Screening. JAMA Internal Medicine 2022, 182: 1306-1312. PMID: 36342705, PMCID: PMC10623674, DOI: 10.1001/jamainternmed.2022.4969.Peer-Reviewed Original ResearchCurrent 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 ResearchMeSH KeywordsArtificial IntelligenceFemaleHumansInfertilityMachine LearningPregnancyReproductive MedicineUse 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 ResearchPerspectives of Patients About Artificial Intelligence in Health Care
Khullar D, Casalino LP, Qian Y, Lu Y, Krumholz HM, Aneja S. Perspectives of Patients About Artificial Intelligence in Health Care. JAMA Network Open 2022, 5: e2210309. PMID: 35507346, PMCID: PMC9069257, DOI: 10.1001/jamanetworkopen.2022.10309.Peer-Reviewed Original ResearchPublic vs physician views of liability for artificial intelligence in health care
Khullar D, Casalino LP, Qian Y, Lu Y, Chang E, Aneja S. Public vs physician views of liability for artificial intelligence in health care. Journal Of The American Medical Informatics Association 2021, 28: 1574-1577. PMID: 33871009, PMCID: PMC8279784, DOI: 10.1093/jamia/ocab055.Peer-Reviewed Original Research
2025
AGA Living Clinical Practice Guideline on Computer-Aided Detection–Assisted Colonoscopy
Sultan S, Shung D, Kolb J, Foroutan F, Hassan C, Kahi C, Liang P, Levin T, Siddique S, Lebwohl B. AGA Living Clinical Practice Guideline on Computer-Aided Detection–Assisted Colonoscopy. Gastroenterology 2025, 168: 691-700. PMID: 40121061, DOI: 10.1053/j.gastro.2025.01.002.Peer-Reviewed Original ResearchConceptsSessile serrated lesion detection rateSerrated lesion detection rateDetection of colorectal polypsSystematic reviewAdenoma detection rateIntensive surveillance colonoscopyPreferences of patientsClinical practice guidelinesSystematic review of studiesPatient-important outcomesCertainty of evidenceAmerican Gastroenterological Association (AGAAdvanced adenomasSurveillance colonoscopyComputer-aided detectionEffect of computer-aided detectionColorectal polypsProvider trustGuideline methodologistsRecommendations AssessmentReview of studiesMultidisciplinary panelColonoscopyIncreasing burdenContent expertsArtificial 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.The development of an artificial intelligence auto-segmentation tool for 3D volumetric analysis of vestibular schwannomas
Jester N, Singh M, Lorr S, Tommasini S, Wiznia D, Buono F. The development of an artificial intelligence auto-segmentation tool for 3D volumetric analysis of vestibular schwannomas. Scientific Reports 2025, 15: 5918. PMID: 39966622, PMCID: PMC11836447, DOI: 10.1038/s41598-025-88589-x.Peer-Reviewed Original ResearchConceptsGround-truth datasetDice scoreVestibular schwannomaImage processing accuracyVolumetric analysisML-based algorithmsMeasuring tumor sizeMean dice scoreAuto-segmentation toolAccurate AIAI modelsTumor sizeTumor modelVS tumorsTumor growthTesting stageAI-LTumorImage processing softwareClinical practicePatient recruitmentProcessing softwareSchwannomaDatasetManual segmentationCould metabolic imaging and artificial intelligence provide a novel path to non-invasive aneuploidy assessments? A certain clinical need
Horta F, Sakkas D, Ledger W, Goldys E, Gilchrist R. Could metabolic imaging and artificial intelligence provide a novel path to non-invasive aneuploidy assessments? A certain clinical need. Reproduction Fertility And Development 2025, 37 PMID: 39874158, DOI: 10.1071/rd24122.Peer-Reviewed Original ResearchMeSH KeywordsAneuploidyArtificial IntelligenceFemaleGenetic TestingHumansPregnancyPreimplantation DiagnosisConceptsPGT-AMetabolic imagingAneuploid embryosEmbryo biopsyEmbryo selectionNon-invasive PGT-APre-implantation genetic testingEnhancing embryo selectionNon-invasive metabolic imagingAneuploidy assessmentEmbryo ploidyGenetic testingInvasive natureClinical practicePotential mosaicismClinical needBiopsyFluorescence lifetime imaging microscopyTreatment costsAneuploidyActivation signatureMeasuring cellular metabolismHuman fibroblastsNon-invasive technologyDiagnostic practiceIntegrating 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 dynamicsThe evolving role of liver biopsy: Current applications and future prospects
Gopal P, Hu X, Robert M, Zhang X. The evolving role of liver biopsy: Current applications and future prospects. Hepatology Communications 2025, 9: e0628. PMID: 39774070, PMCID: PMC11717517, DOI: 10.1097/hc9.0000000000000628.Peer-Reviewed Original Research
2024
Can Artificial Intelligence Deceive Residency Committees? A Randomized Multicenter Analysis of Letters of Recommendation
Simister S, Huish E, Tsai E, Le H, Halim A, Tuason D, Meehan J, Leshikar H, Saiz A, Lum Z. Can Artificial Intelligence Deceive Residency Committees? A Randomized Multicenter Analysis of Letters of Recommendation. Journal Of The American Academy Of Orthopaedic Surgeons 2024, 33: e348-e355. PMID: 39693540, DOI: 10.5435/jaaos-d-24-00438.Peer-Reviewed Original ResearchMeSH KeywordsArtificial IntelligenceCorrespondence as TopicHumansInternship and ResidencyOrthopedicsPersonnel SelectionSingle-Blind MethodArtificial intelligence in ophthalmology
Khossravi A, Chen Q, Adelman R. Artificial intelligence in ophthalmology. Current Opinion In Ophthalmology 2024, 36: 35-38. PMID: 39607311, DOI: 10.1097/icu.0000000000001111.Peer-Reviewed Original ResearchMeSH KeywordsArtificial IntelligenceDiagnostic Techniques, OphthalmologicalEye DiseasesHumansOphthalmologyConceptsTreatment of diabetic retinopathyImprove accuracy of diagnosisProgression of diseasePersonalized treatment plansCorneal infectionVisual outcomeIntraocular pressureMacular degenerationDiabetic retinopathyAccuracy of diagnosisFollow-upTreatment planningOcular imagingPersonalization of medicinePredictive of progression of diseasePatient outcomesDiagnosisEnhance patient outcomesIncorporation of genomicsTreatmentHome monitoringOutcomesCornealGlaucomaEctasiaArtificial Intelligence in Cardiovascular Clinical Trials
Cunningham J, Abraham W, Bhatt A, Dunn J, Felker G, Jain S, Lindsell C, Mace M, Martyn T, Shah R, Tison G, Fakhouri T, Psotka M, Krumholz H, Fiuzat M, O’Connor C, Solomon S, Collaboratory H. Artificial Intelligence in Cardiovascular Clinical Trials. Journal Of The American College Of Cardiology 2024, 84: 2051-2062. PMID: 39505413, DOI: 10.1016/j.jacc.2024.08.069.Peer-Reviewed Original ResearchMeSH KeywordsArtificial IntelligenceCardiovascular DiseasesClinical Trials as TopicHumansRandomized Controlled Trials as TopicConceptsArtificial intelligenceIntegrate AIPatient privacyClinical trialsRandomized clinical trialsClinical event outcomesCardiovascular clinical trialsIntelligenceInaccurate resultsRandomized trialsInterpreting imagesCardiovascular therapyMedical decision makingDecision makingGold standardValidity of trial resultsClinical trial operationsPrivacyCoronary artery calcium measurement on attenuation correction computed tomography using artificial intelligence: correlation with coronary flow capacity and prognosis
Cho S, Lee J, Cho K, Park K, Kim J, Moon J, Kim K, Kim J, Song H. Coronary artery calcium measurement on attenuation correction computed tomography using artificial intelligence: correlation with coronary flow capacity and prognosis. European Journal Of Nuclear Medicine And Molecular Imaging 2024, 52: 1050-1059. PMID: 39404786, PMCID: PMC11754321, DOI: 10.1007/s00259-024-06948-8.Peer-Reviewed Original ResearchConceptsCoronary flow capacityCoronary artery calciumMyocardial blood flowLeft ventricular ejection fraction < 40%Significant ischaemiaStress myocardial blood flowEjection fraction < 40%Coronary artery calcium measurementAcute coronary syndromeNonfatal myocardial infarctionCoronary stent insertionPrognostic significancePrognostic valueStent insertionArtery calciumPrognostic associationBypass surgeryCalcium measurementsHeart failureCardiovascular eventsCoronary syndromeAngina pectorisMultivariate analysisRisk factorsPatients
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