About the Journal

Editorial Board


Khaled El Emam, PhD

Canada Research Chair in Medical AI, University of Ottawa; Senior Scientist, Children’s Hospital of Eastern Ontario Research Institute: Professor, School of Epidemiology and Public Health, University of Ottawa (Canada)

Dr. Khaled El Emam is the Canada Research Chair (Tier 1) in Medical AI at the University of Ottawa, where he is also a Professor in the School of Epidemiology and Public Health. He is also a Senior Scientist at the Children’s Hospital of Eastern Ontario Research Institute and Director of the multi-disciplinary Electronic Health Information Laboratory, conducting research on privacy enhancing technologies to enable the sharing of health data for secondary purposes, including synthetic data generation and de-identification methods. 

Dr. El Emam (co-)founded multiple product companies based on his research at the university, all with successful exits. Prior to his academic roles, he was a Senior Research Officer at the National Research Council of Canada. He also served as the head of the Quantitative Methods Group at the Fraunhofer Institute in Kaiserslautern, Germany. Khaled held the Canada Research Chair in Electronic Health Information at the University of Ottawa from 2005 to 2015.

 More information about his work is available at https://ehealthinformation.ca/

Bradley Malin, PhD

Accenture Professor of Biomedical Informatics, Biostatistics, and Computer Science; Vice Chair for Research Affairs, Department of Biomedical Informatics: Affiliated Faculty, Center for Biomedical Ethics & Society, Vanderbilt University Medical Center, Nashville, Tennessee (USA)

Bradley Malin's research is funded through various grants from the National Institutes of Health (NIH), National Science Foundation (NSF), and Patient Centered Outcomes Research Institute (PCORI) to construct technologies that enable artificial intelligence and machine learning applications (AI/ML) in the context of real world organizational, political, and health information architectures. To build practical solutions, his work draws upon methodologies in computer science, biomedical science, and public policy to innovate novel computational techniques. He has made specific contributions to a number of health-related areas, including distributed data processing methods for medical record linkage and predictive modeling, intelligent auditing technologies to protect electronic medical records from misuse in the context of primary care, and algorithms to formally anonymize patient information disseminated for secondary research purposes.

Dr Malin co-directs the AI Discovery and Vigilance to Accelerate Innovation and Clinical Excellence (ADVANCE) Center at Vanderbilt, as well as the Center for Genetic Privacy and Identity in Community Settings (GetPreCiSe) - an NIH Center of Excellence on Ethical, Legal, and Social Implications Research, the Infrastructure Core of the NIH Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD), and the Ethical and Trustworthy AI Core of the NIH Bridge2AI Center. In addition, he currently serves as the co-chair of the Committee on Access, Privacy, and Security (CAPS) of the All of Us Research Program of the U.S. Precision Medicine Initiative, an appointed member of the Technical Anonymisation Group of the European Medicines Agency, and an appointed member of the Board of Scientific Counselors of the National Center for Health Statistics of the Centers for Disease Control and Prevention.

Editorial Board Members

Associate Editors

Alessandro Blasimme, PhD

Visiting Professor, La Sapienza University of Rome (Italy), Senior Scientist in Ethics and Epistemology of Medicine – Department of Health Sciences and Technology – ETH Zurich.

Alessandro Blasimme is a bioethicist at Swiss Federal Institute of Technology (ETH Zürich). He has held research appointments at the French National Institute of Health and Medical Research (INSERM) as well as the University of Zürich, before joining ETH Zürich. Previously, he received a Fulbright-Schuman Scholarship to undertake research at the Program on Science, Technology and Society, Harvard University (USA) where he was a fellow. His research is at the forefront of academic debates about the ethical and policy implications of digitalization and AI in medicine and biomedical research. 

Yuankai Huo, PhD

Assistant Professor, Department of Computer Science, Department of Electrical and Computer Engineering, Data Science Institute, Vanderbilt University, Nashville, Tennessee (USA)

Yuankai Huo's research is supported by the National Science Foundation (NSF), National Institutes of Health (NIH), and Office of Naval Research (ONR) to develop new technologies in medical machine learning, image analysis, and image informatics. Dr. Huo is the co-chair of Machine Learning Tools/Research Committee in Society for Imaging Informatics in Medicine (SIIM). Dr. Huo has served as the chair and organization committee member for various medical image computing and machine learning conferences. 

Hongfang Liu, PhD

Professor, McWilliam School of Biomedical Informatics and Vice President of Learning Health System at the University of Texas Health Science Center at Houston (UTHealth) (USA)

She also serves as the Director of the Center for Translational Excellence and Application in Medicine - Artificial Intelligence (Center for TEAM-AI), leading a team of faculty members, informaticians, and data scientists to advance translational AI in medicine and healthcare.  She has extensive research experience in natural language processing, standards and interoperability, software engineering, clinical research informatics, and translational bioinformatics with over 400 peer-review articles. Her current research missions are to facilitate secondary use of Electronic Health Records (EHRs) for clinical and translational science research and health care delivery improvement through clinical NLP and to deliver open-source informatics solutions for high-throughput phenotyping through team science collaboration and community-wide collaborative projects.

Gang Luo, PhD

Professor, Biomedical Informatics and Medical Education, University of Washington (USA)

Dr. Luo is a Professor in the Department of Biomedical Informatics and Medical Education at the University of Washington. Before that, he was a Research Staff Member at the IBM T.J. Watson research center and a faculty member in the Department of Biomedical Informatics at the University of Utah. His research interests include machine learning, information retrieval, database systems, big data, and health informatics (software system design/development and data analytics). He invented the first method for automatically providing rule-based explanations for any machine learning model's predictions on tabular data with no accuracy loss, the first method for efficiently automating machine learning model selection, the questionnaire-guided intelligent medical search engine iMed, intelligent personal health record, and SQL, machine learning, and compiler progress indicators.

Douglas Manuel, MD, MSc, FRCPC

Distinguished Professor in the Department of Family Medicine and the School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa. 

He holds a Tier 1 Clinical Research Chair in Precision Medicine for Chronic Disease Prevention. He is a Senior Scientist at the Ottawa Hospital Research Institute, a Senior Core Scientist at ICES and a Senior Medical Advisor at Statistics Canada.

Jean Louis Raisaro, PhD

Tenure-track Assistant Professor in Biomedical Informatics and Data Science, Biomedical Data Science Center and Faculty of Biology and Medicine, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland.

Dr. Raisaro is an Assistant Professor and Head of the Clinical Data Science Group at the Biomedical Data Science Center of Lausanne University Hospital, Lausanne, Switzerland. He is an expert in the development of new technologies to enable safe and privacy-preserving analytics and machine learning on health data for secondary purposes in compliance with stringent legal and ethical frameworks. He has made specific contributions in several areas, including privacy-preserving methods for processing medical data in distributed or outsourced environments, patient-centered data sharing tools, semantic data interoperability, and AI-based predictive modeling for clinical decision making. Besides his research activities, Dr. Raisaro is also spearheading the institutional effort in translating AI-based solutions into practice with particular focus on sepsis, nosocomial complications in old inpatients, medical coding, patient flow management, low-value care, patient-reported outcome measures.

Jimeng Sun, PhD

Health Innovation Professor, Department of Computer Science, Carle Illinois College of Medicine, University of Illinois Urbana-Champaign (USA)

Previously, he was at the College of Computing at Georgia Institute of Technology. His research interest is on artificial intelligence (AI) for healthcare: Deep learning for drug discovery, Clinical trial optimization, Computational phenotyping, Clinical predictive modeling, Treatment recommendation, Health monitoring."

Danica Xiao, PhD

Cao (Danica) Xiao is the Vice President of AI at GE Healthcare.

Cao (Danica) Xiao is the VP of AI at GE Healthcare. Previously she has held leadership positions at multiple technology and healthcare companies, including VP of AI/ML at Relativity, Senior Director of Machine Learning of Amplitude, Global Head of Machine Learning of IQVIA, and research lead of MIT-IBM Watson AI Lab at IBM Research. 

She has successfully driven the creation of machine learning innovation for industry AI transformation, particularly in healthcare and medicine. Besides, she is a passionate machine learning researcher and thought leader with over 120+ highly cited papers (citation 8352, h-index 39, i10-index 81) published in leading AI/ML venues, with strong focus on the topic of AI for healthcare (EHR analysis, RWE, clinical trials, health monitoring, medical imaging, and drug discovery ). She also co-authored a textbook on deep learning for healthcare which is used in top CS graduate programs such as UIUC, GaTech, and PSU. Recently she was named by the largest Chinese internet company Baidu as “Top Chinese Young Scholars in Artificial Intelligence” in 2022 and “Top Chinese Female Scholars in Artificial Intelligence” in 2023. Danica got her Ph.D. degree in machine learning from University of Washington, Seattle in 2016.

Zhijun Yin, MS, PhD

Assistant Professor of biomedical informatics and computer science at Vanderbilt University Medical Center, Nashville, Tennessee, (USA)

Dr. Yin’s research focuses on consumer health, clinical informatics, and aims to design and apply machine learning, natural language processing, and statistical inference to understand and predict health-related outcomes and behaviors using either electronic health records or social media data. Dr. Yin is a Principal investigator and co-Investigator in several NIH-funded research projects, with a focus on prediction models, ethical, bias and fairness of AI in healthcare application domain.

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This may be particularly interesting for workshop and conference organizers putting together a grant-funded event (eg, with invited experts) on an AI-related topic. JMIR AI can then be used as a dissemination vehicle. (Funding through grants or other sources is usually required and should be budgeted for in grant proposals. Letters of support are available from the JMIR  AI editors. Note that granting agencies such as NLM or CIHR usually want to see some sort of knowledge translation activities in workshop proposals, and have in the past funded the JMIR APFs.)

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