Moderator: Dr. Mindy Yang, MD
Breast Radiologist, Imaging Informacist,
Dept. of Radiology, Englewood Hospital,
Clinical Director of AI,
Radiology Partners,
Kailuea, Hawaii,
the USA

Dr. Mindy Yang is a clinical breast imaging radiologist and imaging informaticist, with a strong passion for integrating innovative technologies to advance patient care. Her expertise spans teleradiology with an emphasis on remote breast imaging, as well as the application of AI and imaging informatics to improve workflow, communication, and outcomes. She is a Clinical Director of AI for Radiology Partners and contributes as a thought leader, speaker and moderator for webinars, panels, and national radiology conferences. She is the chair of the Society for Imaging Informatics in Medicine (SIIM) Diversity, Equity, and Inclusion Committee. Dr. Yang received the 2023 Dr. Ruth Dayhoff Award for the Advancement of Women in Medical Imaging Informatics.

Panelist: Prof. Pranav Rajpurkar, PhD
Associate Professor of Biomedical Informatics,
Harvard University, Blavatnik Institute, Biomedical Informatics,
Co-founder of a2z Radiology AI.
Boston, Massachusetts, the USA

Dr. Pranav Rajpurkar is Co-founder of a2z Radiology AI and Associate Professor at Harvard Medical School. A Stanford-trained computer scientist, he develops AI systems for comprehensive medical decision making. His 130 plus academic research papers have garnered over 39,000 citations and recognition from MIT Tech Review, Forbes 30 Under 30, and Nature Medicine.

Panelist: Prof. Curtis P. Langlotz, MD, PhD, FACMI, FSIIM
Senior Associate Vice Provost for Research,
Professor of Radiology (Integrative Biomedical Imaging Informatics),
of Medicine (BMIR), of Biomedical Data Science,
Senior Fellow at the Stanford Institute for Human-Centered AI,
Stanford University, Stanford, California
President of the Radiological Society of North America (RSNA) Board of Directors,
the USA

Dr. Langlotz is a Professor of Radiology, Medicine, and Biomedical Data Science, a Senior Fellow at the Institute for Human-Centered Artificial Intelligence, and Senior Associate Vice Provost for Research at Stanford University. He also serves as Director of the Center for Artificial Intelligence in Medicine and Imaging (AIMI Center), which supports over 250 faculty at Stanford who conduct interdisciplinary machine learning research to improve clinical care. Dr. Langlotz’s NIH-funded laboratory develops machine learning methods to detect disease and eliminate diagnostic errors. He has led many national and international efforts to improve medical imaging, including the RadLex standard terminology system and the Medical Imaging and Data Resource Center (MIDRC), a U.S. national imaging research resource.

Panelist: Prof. Kenji Suzuki,PhD
Professor of Biomedical Artificial Intelligence
Director, BioMedical Artificial Intelligence Research Unit (BMAI),
Institute of Integrated Research (IIR),
Institute of Science Tokyo,
Yokohama, Japan

Prof. Kenji Suzuki, Ph.D. worked at Hitachi Medical Corp, Aichi Prefectural University, Japan, as a faculty member, in the Department of Radiology, University of Chicago, as Assistant Professor, and Medical Imaging Research Center, Illinois Institute of Technology, as Associate Professor (Tenured). He is currently a Full Professor (Tenured) & Founding Director of the Biomedical Artificial Intelligence Research Unit, Institute of Integrated Research, Institute of Science Tokyo (previously Tokyo Institute of Technology), Japan.

Prof. Suzuki’s research interests include deep/machine learning, medical imaging, computer-aided diagnosis, and artificial intelligence (AI).He has published more than 430 papers (including 130 peer-reviewed journal papers such as IEEE TPAMI (Impact Factor: 23.6)). He has been actively researching deep learning in medical imaging and AI-aided diagnosis for the past 25 years—his early deep-learning model was proposed in 1994. His papers have been cited more than 17,000 times, and his h-index is 65.

Prof. Suzuki is the inventor on 40 patents (including some of the earliest deep-learning patents), which were licensed to several companies and commercialized via FDA approvals. He has published 16 books and edited 20 journal special issues. He has been awarded numerous grants including NIH, NEDO, and JST grants, totaling $9.1M.

Prof. Suzuki serves as Editor for more than 20 leading international journals including Pattern Recognition (IF: 7.6) and is the Editor-in-Chief for AI (IF: 5.0). He has chaired 110 international conferences, including being one of the founding chairs of MICCAI MLMI. His research has been covered in 55 articles in newspapers, magazines, and journals by the press and media, including Lancet Respiratory Medicine (IF: 76.2).

He is a Fellow of IARIA. Prof. Suzuki has received 27 awards, including 3 Best Paper Awards in leading journals such as EJNMMI (IF: 10.0), Magna Cum Laude and Cum Laude Awards at RSNA, and the 2021 Award for Science and Technology at the Commendation for Science & Technology by the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan for his pioneering research in deep learning and its applications in the medical imaging field.