Publications — Radcliffe Department of Medicine

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Artificial intelligence-enhanced patient evaluation: bridging art and science

Oikonomou EK. and Khera R., (2024), European Heart Journal

Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice: JACC State-of-the-Art Review

Khera R. et al, (2024), Journal of the American College of Cardiology, 84, 97 - 114

Designing medical artificial intelligence systems for global use: focus on interoperability, scalability, and accessibility

Oikonomou EK. and Khera R., (2024), Hellenic Journal of Cardiology

Using Artificial Intelligence to Predict Heart Failure Risk from Single-lead Electrocardiographic Signals: A Multinational Assessment.

Dhingra LS. et al, (2024)

Development and multinational validation of an algorithmic strategy for high Lp(a) screening

Aminorroaya A. et al, (2024), Nature Cardiovascular Research

Real-world evaluation of an algorithmic machine-learning-guided testing approach in stable chest pain: a multinational, multicohort study

Oikonomou EK. et al, (2024), European Heart Journal - Digital Health

Scalable Risk Stratification for Heart Failure Using Artificial Intelligence applied to 12-lead Electrocardiographic Images: A Multinational Study

Dhingra LS. et al, (2024)

A Multicenter Evaluation of the Impact of Procedural and Pharmacological Interventions on Deep Learning-based Electrocardiographic Markers of Hypertrophic Cardiomyopathy

Dhingra LS. et al, (2024)

A Novel Digital Twin Strategy to Examine the Implications of Randomized Control Trials for Real-World Populations

Thangaraj P. et al, (2024)

Artificial intelligence-enhanced risk stratification of cancer therapeutics-related cardiac dysfunction using electrocardiographic images

Oikonomou E. et al, (2024)

Artificial intelligence-guided detection of under-recognized cardiomyopathies on point-of-care cardiac ultrasound

Oikonomou E. et al, (2024)

Computational Phenomapping of Randomized Clinical Trials to Enable Assessment of their Real-world Representativeness and Personalized Inference

Thangaraj P. et al, (2024)

Evaluation of a Machine Learning-guided Strategy for Elevated Lipoprotein(a) Screening in Health Systems

Aminorroaya A. et al, (2024)

Study Protocol for the Artificial Intelligence-Driven Evaluation of Structural Heart Diseases Using Wearable Electrocardiogram (ID-SHD)

Aminorroaya A. et al, (2024)

RCT-Twin-GAN Generates Digital Twins of Randomized Control Trials Adapted to Real-world Patients to Enhance their Inference and Application.

Thangaraj PM. et al, (2023), medRxiv

A digital biomarker for aortic stenosis development and progression using deep learning for two-dimensional echocardiography.

Oikonomou EK. et al, (2023)

Biometric Contrastive Learning for Data-Efficient Deep Learning from Electrocardiographic Images.

Sangha V. et al, (2023), medRxiv

Severe aortic stenosis detection by deep learning applied to echocardiography.

Holste G. et al, (2023), Eur Heart J

An explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized controlled trials

Oikonomou EK. et al, (2023)

Study Protocol for the Pilot Evaluation for SMartphone-adaptable Artificial Intelligence for PRediction and DeTection of Left Ventricular Systolic Dysfunction (The SMART-LV Pilot Study Protocol)

Dhingra LS. et al, (2023)

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