Associate Professor Johan Verjans

A/Prof Johan Verjans is a clinician-scientist at the Royal Adelaide Hospital with a research focus on cross-disciplinary translational research with a track record in leadership. In his role at the Australian Institute for Machine Learning (AIML), a world-renowned institute in Machine Learning, and as platform leader AI 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 and was in 2022 recipient of the University's Future Industry Making Fellowship.

Since then, he has formed the Medical Machine Learning group at AIML and used his combined clinical, biomedical and technical expertise to lead a rapidly growing group of clinicians, biomedical researchers and computer scientists with the objective of making 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 25 members, won international technology challenges and has contracts with pharma and technology companies such as GSK, Roche, Siemens Healthineers and Medtronic. The group's efforts have caught international attention, leading to an invitation from a renowned consortium of Medical AI Institutes. A testament to their diligence, their institute was distinguished as the AI Centre in Medicine of the Year in 2022 by the AI Global Summit. His interest in AI/ML, accumulated over the past 9 years, reflects an aim to weave machine learning techniques into clinical realms, and he's collaborating with governmental entities to achieve this. He advises companies and is also a member of the global GSK AI advisory board.

His research has been published in leading cardiology journals Circulation, JACC, JAMA Cardiology, Nature Reviews Cardiology, JACC CV Imaging, Light: Science and Applications, IEEE Transaction in Medical Imaging. The most significant achievement in computer science is that his team won the global Medical VQA challenge organised by the NIH. VQA was at that time one of the most challenging areas in machine learning, whereby the computer is programmed to answer open-ended questions from medical images, which was the early version of medical chatGPT.

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 a member of the Publications Committee of the Society for Cardiac MR evaluating clinical imaging guidelines and consensus statements. He was the lead author of 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 editorial board member of the European Heart Journal Digital Health and Frontiers in Cardiology and is Associate Editor of the Netherlands Heart Journal and a member of the publication committee in a leading imaging society, the Society of Cardiac MRI.

Mentorship-wise, he provides invaluable guidance to budding researchers and students, creating a nurturing academic environment with a fantastic mix of engineers, clinicians, and computer scientists.