Special Issue on Intelligent Systems in Health: Challenges and Opportunities
IEEE Intelligent Systems
Special Issue on Intelligent Systems in Health: Challenges and Opportunities
Call for Papers
Healthcare is a societal need, and a huge part of our lives, both personal and professional. Today, health spending accounts for 17.7% of the Gross Domestic Product (GDP) of the USA. Indeed, the gross output of the U.S. health care and social assistance industry has increased from $870 billion in 1998 to over $2.5 trillion in 2018. Furthermore, there are new challenges such as new and evolving diseases (e.g., antibiotic resistant bacteria), evolving situations (e.g., COVID-19 pandemic), evolving behavior (e.g., anti-vaxxer movement, sedentary lifestyle, opioid crises) that require constant innovation in therapeutic treatments, drug discovery, and other aspects.
There is immense potential for transformative changes in the health and biomedical research community through the adoption of new technologies such as IoT devices, cyber-physical systems, mobile and Edge/Cloud computing, Social Networks, and Artificial Intelligence / Machine Learning. Together, the use of these technologies and platforms can help us to realize the dream of personalized health and preventive services. However, the burgeoning data-intensive, hyper-connected system of interacting technologies that store, manage, exchange, combine, and utilize sensitive data can be vulnerable to malicious use. Altogether, the use of intelligent computing technologies and systems, while transformational, brings a plethora of security, privacy, trust, and ethical challenges that are unique to health and biomedical research.
This special issue aims to develop a comprehensive vision and gather recent advances in ensuring security, privacy, and ethics in intelligent systems and their applications to health and biomedical research. The special issue will feature novel research on several areas of intelligent systems and data frameworks including wearables and connected devices, big data analytics and AI/ML technologies in health and biomedical informatics, and the use of real-time multi-modal data for health applications. Besides these, the special issue will focus on research work with compelling results from real-world case studies and successes/failures in emerging applications to health and biomedical research.
The contributions should address, but are not limited to, the following research issues/topics, with a specific focus on application in health and biomedical informatics:
- Big Data Analytics and AI/ML in Health and Biomedical Informatics
- Adversarial Machine Learning
- Ethics and Societal aspects of Big Data Analytics and Inequities in Care
- Ethics of Autonomous AI systems for Health
- Bias, Replicability and Provenance of Analytics Workflows
- Issues and concerns with use of open source tools and technologies
- Healthcare as a service
- Real-time Multi-modal data
- Personalized medical treatment
- Life-cycle issues for data
- Acquisition, Integration, Sharing of Multimedia Data
- Data Collection Bias and Data Quality
- Wearables and Connected Devices
- Ownership of Data
- Privacy Agreements and Data Authorization
- Firmware security for legacy devices
- Standardization and Decommissioning
- Life-cycle issues for devices
- Information Flow and Data Sharing in Health and Biomedical Informatics
- Information authenticity and provenance
- Workflow Security
Submitted articles must not have been previously published or currently submitted for journal or magazine publication elsewhere, and must be a significant contribution to the state of the art in intelligent systems.
All the submissions will be judged on novelty of the proposed problem and/or solution, technical relevance and practicality of presented ideas, relevance of the addressed topic to the scope of the Special Issue, writing and presentation, accuracy and impact of the results. As an author, you are responsible for understanding and adhering to the IEEE Intelligent Systems submission guidelines, which are available at https://www.computer.org/publications/author-resources/peer-review/magazines and Author Information available at https://www.computer.org/csdl/magazine/ex/write-for-us/14365?title=Author%20Information&periodical=IEEE%20Intelligent%20Systems
Please, carefully read these guidelines before submitting your manuscript. In the case the manuscript extends previous work, please make sure to 1) cite the previous paper(s); 2) clearly explain in the paper introduction and related work Sections what the contributions of the submitted paper are, and why they are significant; 3) by way of points 1 and 2, explain how the paper submitted this SI of IEEE IS extends the previous work; 4) any material taken and used from the conference paper, or any other paper, must be properly attributed. That means the text must be quoted and cited and referenced. Permission for including figures that appear elsewhere must be obtained from the copyright owner.
To ensure proper submission, log in to the manuscript central for the IEEE Intelligent Systems (https://mc.manuscriptcentral.com/is-cs) and select “Special Issue on Intelligent Systems in Health: Challenges and Opportunities” as the manuscript type.
Paper submission deadline:
31 October 2021
All reviews back and first-round notification:
31 December 2021
Revised submission deadline:
31 January 2022
All reviews back and final notification:
31 March 2022
31 April 2022
Guest Editors (GEs)
Jaideep Vaidya, Ph.D.
Jaideep Vaidya is a Professor in the MSIS Department at Rutgers University and the Director of the Rutgers Institute for Data Science, Learning, and Applications. He received the B.E. degree in Computer Engineering from the University of Mumbai, the M.S. and Ph.D. degree in Computer Science from Purdue University. His general area of research is in security, privacy, data mining, and data management. He has published over 190 technical papers in peer-reviewed journals and conference proceedings, and has received several best paper awards from the premier conferences in data mining, databases, digital government, security, and informatics. He is an IEEE Fellow, ACM Distinguished Scientist, and is the Editor in Chief of the IEEE Transactions on Dependable and Secure Computing.
Steven Steinhubl, MD
Steven Steinhubl is director of Digital Medicine at Scripps Research Translational Institute and a cardiologist at Alaska Native Tribal Health Consortium. He received his undergraduate training in Chemical Engineering at Purdue University in Indiana, graduate training in Physiology at Georgetown University in Washington, DC, and his medical degree at St. Louis University in Missouri. Steinhubl’s internal medicine residency training was completed at David Grant Medical Center at Travis Air Force Base, California. His cardiology and interventional cardiology fellowships were at the Cleveland Clinic Foundation. Prior to joining the Translational Institute, Steinhubl was director of Cardiovascular Wellness and the medical director for Employee Wellness for the Geisinger Healthcare System.
As head of digital medicine at the Translational Institute, Steinhubl leads a research team in the design, development and management of clinical programs built specifically around the novel capabilities of a wide range of digital technologies, including wearable sensors, and big data analytics. His team focuses on the re-imagining of health and healthcare that is made possible through the evidence-based incorporation of digital technologies.
Steinhubl has been active in clinical research for almost 20 years and has been the principal investigator of dozens of national and international trials and has published over 270 peer-reviewed manuscripts.