Rickey Carter

Professor, Biostatistics

Mayo Clinic

United States

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Rickey Carter is a Professor of Biostatistics at the Mayo Clinic, located in the United States. His research primarily focuses on the application of statistical methods in healthcare, particularly in the areas of diabetes management, heart failure evaluation, and the integration of artificial intelligence in medical diagnostics. Recent publications highlight his work on topics such as nocturnal gluconeogenesis in Type 2 diabetes and the use of AI in detecting cardiac dysfunction.

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2025

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2021

Collaborators (8)

Jeffrey Henderson

Associate Professor

Washington University in St. Louis

UNITED STATES

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Ryan J. Tedford

Medical University of South Carolina

UNITED STATES

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Frederik Verbrugge

Clinical Professor

Vrije Universiteit Brussel

BELGIUM

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Katelyn Bruno

University of Florida

UNITED STATES

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Zachi I Attia

UNITED STATES

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Francisco Lopez-Jimenez

UNITED STATES

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Barry Borlaug

Professor

Mayo Clinic

UNITED STATES

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Fernando Bril

The University of Alabama

UNITED STATES

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Rickey Carter

Research Interests

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About

Articles (20)

Role of Hepatic Glycogen on Nocturnal Gluconeogenesis in Type 2 Diabetes Mellitus

Evidence-Based Application of Natriuretic Peptides in the Evaluation of Chronic Heart Failure With Preserved Ejection Fraction in the Ambulatory Outpatient Setting

Estimation of Static Lung Volumes and Capacities From Spirometry Using Machine Learning: Algorithm Development and Validation

AI-Augmented Kidney Stone Composition Analysis with Auto-Release Improves Quality, Efficiency, Cost–Effectiveness, and Staff Satisfaction

Testing the real-world utility of Bayes theorem in artificial intelligence-enabled electrocardiogram algorithm for the detection of left ventricular systolic dysfunction

An evidence-based screening tool for heart failure with preserved ejection fraction: the HFpEF-ABA score

Estimates of actual and potential lives saved in the United States from the use of COVID-19 convalescent plasma

Impact of Demographics and Psychological Factors on Three-Day Postoperative Pain Perception Following Hand Surgery

Diagnosing Allergic Contact Dermatitis Using Deep Learning: Single-Arm, Pragmatic Clinical Trial with an Observer Performance Study to Compare Artificial Intelligence Performance with Human Reader Performance

Dapagliflozin Enhances Arterial and Venous Compliance During Exercise in Heart Failure With Preserved Ejection Fraction: Insights From the CAMEO-DAPA Trial

Cardiac and Metabolic Effects of Dapagliflozin in Heart Failure With Preserved Ejection Fraction: The CAMEO-DAPA Trial

A Review of Voice-Based Pain Detection in Adults Using Artificial Intelligence

Perceived Age in Patients Exposed to Distinct UV Indexes: A Systematic Review

Artificial Intelligence for Kidney Stone Spectra Analysis: Using Artificial Intelligence Algorithms for Quality Assurance in the Clinical Laboratory

Late Treatment for COVID-19 With Convalescent Plasma

Concerns about estimating relative risk of death associated with convalescent plasma for COVID-19

Real-world performance, long-term efficacy, and absence of bias in the artificial intelligence enhanced electrocardiogram to detect left ventricular systolic dysfunction

Clinical and laboratory profiles of the SARS-CoV-2 Delta variant compared with pre-Delta variants

Model drift: When it can be a sign of success and when it can be an occult problem

Electrocardiogram screening for aortic valve stenosis using artificial intelligence

Collaborators (8)

Jeffrey Henderson

Ryan J. Tedford

Frederik Verbrugge

Katelyn Bruno

Zachi I Attia

Francisco Lopez-Jimenez

Barry Borlaug

Fernando Bril

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