Dr. Johan Verjans, SAHMRI - Vascular Research Centre, Platform AI

Deputy Director, Medical Machine Learning, Australian Institute for Machine Learning

Platform leader AI, SAHMRI

Consultant Cardiologist, Royal Adelaide Hospital / Dr Jones & Partners

Dr Johan Verjans is a clinician-scientist with a research focus on cross-disciplinary translational research with a track record in leadership. In his role as a deputy director at the Australian Institute for Machine Learning (AIML), a world-renowned institute in Machine Learning, and as platform leader artificial intelligence at SAHMRI, he combines experience in molecular medicine and clinical research with vast experience working with engineers for advanced imaging techniques, and computer scientists to apply machine learning to medical problems to translate research into the clinic.

He graduated in medicine from Maastricht University via an MD-PhD track after being awarded the DiPalma Fellowship to work with the renowned Professor Narula in Philadelphia and UC Irvine. During cardiology training, he was awarded a prestigious Rubicon Fellowship by the Dutch Science Foundation to complete a post-doctoral fellowship at Massachusetts General Hospital/Harvard Medical School, followed by a clinician-scientist award at the University Medical Centre Utrecht during his cardiology training. He was recruited by the University of Adelaide in 2017.

Since then, he has formed the Medical Machine Learning group at AIML and using his combined clinical, biomedical and technical expertise to lead a rapidly growing group of clinicians, biomedical researchers and computer scientists with the objective to make the University of Adelaide a global leader in AI in Healthcare, using existing globally renowned AI expertise at AIML. The group has grown to over 20 members, won international technology challenges and has contracts with pharma and technology companies. He is an associate investigator of the ARC Centre for Nanoscale Biophotonics.

His research has been published in leading cardiology journals (Circulation (IF 23.6), JACC (IF 20.6: incl editorial), Nature Reviews Cardiology (IF20.3), Basic Research in Cardiology (IF12.6). He has published six articles in the number 1 ranked imaging journal, JACC CV Imaging (IF12.7), including Front Cover Image+Editorial), in addition to other prestigious journals, such as Light: Science and Applications (IF 15) and IEEE Transaction in Medical Imaging (IF13.94). The most significant achievement in computer science is that his team won the global Medical VQA challenge organised by the NIH. VQA is one of the most challenging areas in machine learning, whereby a the computer is programmed to answer open-ended questions from medical images.

He has given presentations at all major cardiovascular conferences, including several young investigator award sessions. He was chair of the Australian Society for Molecular Imaging Conference, and member of the Publications Committee of the Society for Cardiac MR evaluating clinical imaging guidelines and consensus statements. He was lead author on a chapter in the first book of AI in Medicine (Springer/Nature) and contributed to several other chapters, including an international expert group to standardise machine learning methods in Medical Imaging. He is an Associate Editor of the Netherlands Heart Journal (IF 1.9) and a member of the publication committee in a leading imaging society, Society of Cardiac MR. He has reviewed articles for leading cardiology journals JACC, EHJ, JACC CV imaging, and 30+ other journals and is national and state leadership boards in Cardiology and Data & Analytics, including the Big Data Flagship of the Australian Cardiovascular Alliance.

In the past, he has been national chair of Dutch Medical Students, and I have represented students in the national medical association board, followed by being the national chair of Dutch early/mid-career cardiovascular researchers, supported by the Dutch heart Institute and Dutch Heart Foundation.