Ebraham Alskaf, MD

Research Interests: Cardiovascular Imaging, Cardiac Magnetic Resonance, Artificial Intelligence in Medicine, Interventional Cardiology, Outcome Prediction, and Computer Science in Medicine.

Ebraham Alskaf, MD is a post-CCT interventional specialty doctor in cardiology at University Hospitals Sussex, UK, and a PhD candidate in Biomedical Engineering and Imaging Sciences at King's College London, UK. With dual qualifications in cardiology and general internal medicine, Dr. Alskaf has extensive expertise in clinical cardiology, internal medicine, interventional cardiology, and cardiac imaging, particularly in stress perfusion cardiac magnetic resonance (CMR) and artificial intelligence (AI) applications in cardiovascular medicine.

Dr. Alskaf holds multiple accreditations, including European Association of Cardiovascular Imaging (EACVI) Level 3 CMR and British Society of Echocardiography (BSE) full accreditation in adult trans-thoracic echocardiography, and is a member of prominent societies such as the European Society of Cardiology (ESC), the American College of Cardiology (ACC), the Royal College of Physicians (RCP) in London, and the Society for Cardiovascular Magnetic Resonance (SCMR). He has been recognised as a finalist for the prestigious Melvin Judkins Early Career Award at the American Heart Association (AHA) Scientific Sessions in 2023.

His research focuses on AI-driven outcome prediction models in cardiovascular imaging, combining advanced image analysis techniques with electronic health record (EHR) data. Dr. Alskaf's work in deep learning applications for coronary anatomy and myocardial perfusion imaging has led to highly respected publications in journals like Eur J Radiol and Eur Heart J Digit Health. His contributions include authoring peer-reviewed publications and presentations at major international conferences such as the ACC, ESC, and AHA.

Currently involved in several AI-based cardiac imaging research projects, Dr. Alskaf is committed to advancing the integration of machine learning technologies into clinical practice. His work includes hybrid AI models for prognostic assessment of cardiovascular outcomes, utilising techniques like convolutional neural networks and natural language processing of clinical reports. In addition to his clinical and research endeavours, Dr. Alskaf actively contributes to medical education, serving as teaching faculty for international CMR courses.

Dr. Alskaf is excited to serve as Co-Editor-in-Chief of the Journal of Medical Artificial Intelligence, where he aims to foster the growth of AI innovations in medical science and support high-quality research at the intersection of artificial intelligence and medicine.

Dejian Wang, D.Eng

Dr. Dejian Wang is currently the Principal Researcher of Binjiang Institute, Zhejiang University, the Director of the Medical Artificial Intelligence Laboratory at Binjiang Institute, Zhejiang University, and the Founder of Healink Technology Co., Ltd.

Dr. Wang primarily focuses his research on medical artificial intelligence and privacy computing, with a particular specialization in addressing data privacy and model performance challenges in multi-center medical AI modeling. Through innovative breakthroughs in frontier algorithm fields such as federated learning and privacy computing, he has significantly broadened the application scope of artificial intelligence in medical research. This has enabled medical institutions to share value while safeguarding sensitive information, further fostering collaboration, strengthening mutual trust, and driving the ongoing development of medical AI. In addition, Dr. Wang has made remarkable contributions to the field of medical large language models. He is committed to resolving the issues of slow knowledge updates and “hallucinations” in large language models applied to the medical domain to revolutionize how medical knowledge is accessed and utilized, support clinical decision-making, and bridge the gap between advanced AI technology and real-world medical practice.

Dr. Wang has completed artificial intelligence joint modeling work for multiple diseases, such as Pediatrics, Cardiology, Thoracic, Obstetrics and Gynecology, etc, published dozens of high-impact papers, and obtained multiple patents.