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Akhil Vaid

Cardiovascular Institute of the South · US
Area of research
Cardiology and Cardiovascular Medicine · Artificial Intelligence
Research interest
Research interests include ECG Monitoring and Analysis, Machine Learning in Healthcare, COVID-19 Clinical Research Studies, and Artificial Intelligence in Healthcare and Education.
h-index
28
citations
4,093
works
122
NIH funding
primary concept
Medicine
email

Recent publications

Comparing ChatGPT and GPT-4 performance in USMLE soft skill assessments
Scientific Reports 2023cited by 318position: middledoi
Implications of the Use of Artificial Intelligence Predictive Models in Health Care Settings
Annals of Internal Medicine 2023cited by 41position: firstdoi
Multi-center retrospective cohort study applying deep learning to electrocardiograms to identify left heart valvular dysfunction
Communications Medicine 2023cited by 26position: firstdoi
Proteomic characterization of acute kidney injury in patients hospitalized with SARS-CoV2 infection
Communications Medicine 2023cited by 20position: middledoi
Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach
JMIR Medical Informatics 2021cited by 207position: firstdoi
Prevalence and Impact of Myocardial Injury in Patients Hospitalized With COVID-19 Infection
Journal of the American College of Cardiology 2020cited by 745position: middledoi
Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation
Journal of Medical Internet Research 2020cited by 240position: firstdoi
Clinical Characteristics of Hospitalized Covid-19 Patients in New York City
medRxiv 2020cited by 107position: middledoi
Acute Kidney Injury in Hospitalized Patients with COVID-19
medRxiv 2020cited by 86position: middledoi
Retrospective cohort study of clinical characteristics of 2199 hospitalised patients with COVID-19 in New York City
BMJ Open 2020cited by 54position: middledoi
Machine Learning to Predict Mortality and Critical Events in COVID-19 Positive New York City Patients
medRxiv 2020cited by 33position: firstdoi

Grants

No grants ingested yet.

Frequent collaborators

Girish N. Nadkarni · Icahn School of Medicine at Mount Sinai5 papers (2020–2023)Benjamin S. Glicksberg · University of Hong Kong5 papers (2020–2023)Alexander W. Charney · Mount Sinai Hospital4 papers (2020–2023)Zahi A. Fayad · Mount Sinai Hospital3 papers (2020–2023)Jagat Narula · Johannes Gutenberg University Mainz3 papers (2020–2023)Eyal Klang · Beth Israel Deaconess Medical Center3 papers (2021–2023)Edgar Argulian · Mount Sinai Hospital2 papers (2023–2023)Stamatios Lerakis · Mount Sinai Hospital2 papers (2023–2023)Jessica K. De Freitas · Mount Sinai Health System2 papers (2020–2021)Kipp W. Johnson · Elmhurst Hospital Center2 papers (2020–2021)Matthew A. Levin · Mount Sinai Health System2 papers (2020–2023)Ishan Paranjpe · Mount Sinai Hospital2 papers (2020–2021)Shan Zhao · Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine2 papers (2020–2021) · 2 papers (2020–2021)James L. Januzzi · Sentara Heart Hospital1 papers (2020–2020)Chayakrit Krittanawong · House of Representatives1 papers (2023–2023)Adam Russak · Mount Sinai Health System1 papers (2020–2020)Sean Pinney · Advocate Heart Institute1 papers (2020–2020)Arvind Kumar · Birla Institute of Technology, Mesra1 papers (2021–2021)Joy Jiang · Division of Cancer Epidemiology and Genetics1 papers (2023–2023)