Aline Pedroso, PhD
Associate Research ScientistAbout
Titles
Associate Research Scientist
Lead, Scientific Operations, Cardiovascular Data Science (CarDS) Lab
Biography
Dr. Aline Pedroso is a pharmacist and epidemiologist and is the Lead for Scientific Operations at the Cardiovascular Data Science (CarDS) Lab at the Yale School of Medicine. Her research focuses on large-scale studies on cardiovascular risk assessment, particularly in low-resource settings. She also works on studying existing and emerging practices and health policies and their effectiveness in improving patient outcomes. In addition, as the operations lead at the CarDS Lab, she is involved in the design and execution of multicenter studies that deploy digital health and artificial intelligence tools for improving the detection and prognostication of cardiovascular diseases. She manages a broad research portfolio at the CarDS Lab working with a large interdisciplinary team of clinician-scientists and data scientists for the design of innovative health technologies, and manages partnerships with academic, pharmaceutical, and industry partners.
Last Updated on June 02, 2025.
Departments & Organizations
Education & Training
- Postdoctoral Research Associate
- Cardiovascular Data Science (CarDS) Lab (2024)
- PhD
- Federal University of Sao Joao del Rei, Health Sciences (2024)
- Postgraduate Associate
- Cardiovascular Data Science (CarDS) Lab (2023)
- MS
- Federal University of Sao Joal del Rei, Health Sciences (2019)
- BSc
- Federal University of Vales do Jequitinhonha e Mucuri, Pharmacy (2014)
Research
Publications
2025
Access to Electrophysiologic Care for Medicare Beneficiaries Across the United States: Travel Distance and Time to Nearest Clinician, 2013-2020
Khaloo P, Wheelock K, Hanna J, Kapadia S, Pedroso A, Nabi W, Aminorroaya A, Freeman J, Khera R. Access to Electrophysiologic Care for Medicare Beneficiaries Across the United States: Travel Distance and Time to Nearest Clinician, 2013-2020. Heart Rhythm 2025 PMID: 40935055, DOI: 10.1016/j.hrthm.2025.09.013.Peer-Reviewed Original ResearchElectrophysiological careMedicare beneficiariesZip codesPercentage of Hispanic residentsSocio-economically disadvantaged groupsResidents of rural areasAnnual income <Multivariate logistic regression modelHigh school educationLogistic regression modelsUnited StatesUS zip codesSociodemographic factorsCardiovascular careOlder adultsGeographic disparitiesHealthcare ResearchPractitioner dataHispanic residentsLong travel timesMedicare providersPacemaker implantationMedicare PhysicianAF ablationUS countiesTransforming Population Health Screening for Atherosclerotic Cardiovascular Disease with AI-Enhanced ECG Analytics: Opportunities and Challenges
Biswas D, Aminorroaya A, Croon P, Batinica B, Pedroso A, Khera R. Transforming Population Health Screening for Atherosclerotic Cardiovascular Disease with AI-Enhanced ECG Analytics: Opportunities and Challenges. Current Atherosclerosis Reports 2025, 27: 86. PMID: 40888973, DOI: 10.1007/s11883-025-01337-4.Peer-Reviewed Original ResearchConceptsAtherosclerotic cardiovascular diseasePopulation health screeningPopulation-level screeningCardiovascular diseaseLow riskHealth screeningStandard risk factorsHospital-basedCardiovascular healthSubclinical coronary artery diseaseWorkflow integrationSingle-lead ECGPersonalized interventionsPatient outcomesDiverse populationsTraditional risk modelsECG interpretationRisk factorsAscertainment biasImplementation challengesAdverse cardiovascular eventsProspective studyLogistical challengesRe-classifying patientsCoronary artery diseaseNational Patterns of Remote Patient Monitoring Service Availability at US Hospitals.
Pedroso A, Lin Z, Ross J, Khera R. National Patterns of Remote Patient Monitoring Service Availability at US Hospitals. Circulation Cardiovascular Quality And Outcomes 2025, e012034. PMID: 40827414, PMCID: PMC12367071, DOI: 10.1161/circoutcomes.125.012034.Peer-Reviewed Original ResearchRemote patient monitoringUS hospitalsAmerican Hospital Association Annual SurveyTraditional health care settingsRemote patient monitoring servicesHealth care settingsNational studyCounty-level characteristicsCharacteristics of hospitalsMedian household incomeService availabilityMultivariate logistic regressionChronic careRural hospitalsCare settingsNational patternsLongitudinal careRural countiesUrban hospitalsNonteaching hospitalsHospital sizeTeaching statusCounty-level dataLow-incomeDisability statusScientific Writing in the Era of Large Language Models: A Computational Analysis of AI- Versus Human-Created Content
Khera R, Pedroso A, Keloth V, Xu H, Silva G, Schwamm L. Scientific Writing in the Era of Large Language Models: A Computational Analysis of AI- Versus Human-Created Content. Stroke 2025, 56: 3078-3083. PMID: 40814778, DOI: 10.1161/strokeaha.125.051913.Peer-Reviewed Original ResearchConceptsLanguage modelArtificial intelligenceAI-generatedLinguistic featuresDetection toolsAI-generated contentHuman-written textLanguage perplexityHuman expertsPerformance of expertsLinguistic differencesScientific textsGrade levelWord countEssayLanguageScientific communicationScientific writingComputer synthesisHigher grade levelsTextScientific contentReadability scoresPerplexityFlesch-KincaidLeveraging AI-enhanced digital health with consumer devices for scalable cardiovascular screening, prediction, and monitoring
Pedroso A, Khera R. Leveraging AI-enhanced digital health with consumer devices for scalable cardiovascular screening, prediction, and monitoring. Npj Cardiovascular Health 2025, 2: 34. PMID: 40620667, PMCID: PMC12221986, DOI: 10.1038/s44325-025-00071-9.Peer-Reviewed Original ResearchArtificial intelligenceConsumer devicesConsumer wearablesPortable devicesCardiovascular careDigital healthTraditional care settingsDigital health toolsTraditional care modelResource-constrained settingsCare modelPersonalized risk assessmentCare settingsCardiovascular screeningHealth toolsWearableDevicesLow-cost alternativeIntelligenceCareWearable-Echo-FM: An ECG-echo foundation model for single lead electrocardiography.
Knight E, Oikonomou EK, Aminorroaya A, Pedroso AF, Khera R. Wearable-Echo-FM: An ECG-echo foundation model for single lead electrocardiography. MedRxiv 2025 PMID: 40585148, DOI: 10.1101/2025.06.10.25329163.Peer-Reviewed Original Research In PressArtificial Intelligence–Enabled Prediction of Heart Failure Risk From Single-Lead Electrocardiograms
Dhingra L, Aminorroaya A, Pedroso A, Khunte A, Sangha V, McIntyre D, Chow C, Asselbergs F, Brant L, Barreto S, Ribeiro A, Krumholz H, Oikonomou E, Khera R. Artificial Intelligence–Enabled Prediction of Heart Failure Risk From Single-Lead Electrocardiograms. JAMA Cardiology 2025, 10: 574-584. PMID: 40238120, PMCID: PMC12004248, DOI: 10.1001/jamacardio.2025.0492.Peer-Reviewed Original ResearchYale New Haven Health SystemELSA-BrasilPCP-HFNew-onset HFHarrell's C-statisticProspective population-based cohortUK Biobank (UKBBrazilian Longitudinal StudyELSA-Brasil participantsC-statisticPopulation-based cohortIntegrated discrimination improvementReclassification improvementRisk of deathUKB participantsHealth systemRetrospective cohort studyDiscrimination improvementMain OutcomesLeft ventricular systolic dysfunctionHF riskUKBCohort studySingle-lead ECGIndependent of ageThe emerging role of AI in transforming cardiovascular care
Croon P, Pedroso A, Khera R. The emerging role of AI in transforming cardiovascular care. Future Cardiology 2025, 21: 547-550. PMID: 40248957, PMCID: PMC12150600, DOI: 10.1080/14796678.2025.2492973.Peer-Reviewed Original ResearchDevelopment and multinational validation of an ensemble deep learning algorithm for detecting and predicting structural heart disease using noisy single-lead electrocardiograms
Aminorroaya A, Dhingra L, Pedroso A, Shankar S, Coppi A, Khunte A, Foppa M, Brant L, Barreto S, Ribeiro A, Krumholz H, Oikonomou E, Khera R. Development and multinational validation of an ensemble deep learning algorithm for detecting and predicting structural heart disease using noisy single-lead electrocardiograms. European Heart Journal - Digital Health 2025, 6: 554-566. PMID: 40703117, PMCID: PMC12282373, DOI: 10.1093/ehjdh/ztaf034.Peer-Reviewed Original ResearchDetectable structural heart diseaseStructural heart diseaseCommunity-based screeningLeft-sided valvular diseaseHeart diseaseELSA-BrasilYale-New Haven HospitalAI-ECG algorithmDeep learning algorithmsPopulation-based cohortSevere LVHEchocardiographic dataPredictive biomarkersHospital-based sitesNew Haven HospitalRisk stratificationValvular diseaseEnsemble deep learning algorithmUK BiobankCommunity hospitalLead I ECGAutomated Transformation of Unstructured Cardiovascular Diagnostic Reports into Structured Datasets Using Sequentially Deployed Large Language Model
Vasisht Shankar S, Dhingra LS, Aminorroaya A, Adejumo P, Nadkarni GN, Xu H, Brandt C, Oikonomou EK, Pedroso AF, Khera R. Automated transformation of unstructured cardiovascular diagnostic reports into structured datasets using sequentially deployed large language models. Eur Heart J Digit Health. 2025;ztaf030.Peer-Reviewed Original Research
Academic Achievements & Community Involvement
News
News
- January 23, 2025
New AI Tool Identifies Risk of Future Heart Failure
- November 05, 2024
Yale Researchers at American Heart Association Scientific Session 2024
- August 26, 2024
Which Diabetes Meds Are Best for Reducing Heart Attack and Stroke Risk?
- April 01, 2024
Yale Faculty Present Groundbreaking Clinical Research at the 2024 American College of Cardiology Scientific Sessions
Get In Touch
Contacts
Lab Number
Locations
CarDS Lab
Academic Office
195 Church Street, Fl 6th
New Haven, CT 06510