Dr Ahmed El-Medany is a British Heart Foundation Clinical Research Training Fellow and PhD candidate at the National Heart and Lung Institute, Imperial College London. He is a senior cardiology registrar undertaking doctoral research focused on the clinical implementation of artificial intelligence-enhanced electrocardiography for cardiovascular risk assessment and structural heart disease detection.

His research investigates how AI-ECG can be translated from research settings into clinically meaningful pathways, with a particular focus on detecting hidden structural heart disease from standard 12-lead ECGs, portable ECG devices and wearable single-lead recordings. His work spans prospective clinical validation, wearable ECG signal analysis, model generalisability, and the integration of machine-learning tools into NHS diagnostic pathways.

In 2025, Dr El-Medany was awarded a £257,414 British Heart Foundation Clinical Research Training Fellowship to support his doctoral work. This builds on his previous research into autonomic dysfunction in post-COVID-19 syndrome, which led to a first-author publication in Nature Communications Medicine. He has published approximately 21 peer-reviewed articles across primary research, meta-analysis, reviews, editorials and clinical case reports, including work in cardiovascular artificial intelligence, electrophysiology, preventive cardiology and multimodality imaging.

Dr El-Medany’s broader academic interests include digital cardiology, cardiovascular prevention, autonomic physiology, arrhythmia risk stratification, electrophysiology and the safe, equitable implementation of artificial intelligence in healthcare. He holds an MSc with Distinction in Preventive Cardiology from Imperial College London and is a Fellow of the Higher Education Academy.

Alongside his research, Dr El-Medany is actively involved in medical education, postgraduate training, and public and patient involvement. He has supervised Imperial College medical students through the Undergraduate Research Opportunities Programme, delivering teaching in ECG interpretation, Python and machine learning, and leads PPIE activity within the Cardiac AI and Electrophysiology group.