AI and IoT Convergence for Smart Health

Submission Deadline:  31 May 2021

IEEE Access invites manuscript submissions in the area of AI and IoT Convergence for Smart Health.   

With the development of smart sensorial media, things, and cloud technologies, “Smart healthcare” is getting remarkable attention from academia, government, industry, and  healthcare communities. Recently, the Internet of Things (IoT) has brought the vision of a smarter world into reality with a massive amount of data and numerous services. With the outbreak of COVID-19, Artificial Intelligence (AI) has gained significant attention by utilizing its machine learning algorithms for quality patient care. However, the convergence of IoT and AI can provide new opportunities for both technologies. AI-driven IoT can play a significant role in smart healthcare by offering better insight of healthcare data to support affordable personalized care. It can also support powerful processing and storage facilities of huge IoT data streams (big data) beyond the capability of individual “things,” as well as to provide automated decision making in real-time. While researchers have been making advances in the study of AI-and IoT for health services individually, very little attention has been given to developing cost-effective and affordable smart healthcare services. The AI-driven IoT (AIIoT) for smart healthcare has the potential to revolutionize many aspects of our healthcare industry; however, many technical challenges need to be addressed before this potential can be realized.

This Special Section is intended to report high-quality research on recent advances toward AI- and IoT convergence for smart healthcare, more specifically to the state-of-the-art approaches, methodologies, and systems for the design, development, deployment and innovative use of those convergence technologies to provide insight into smart healthcare service demands. Authors are solicited to submit complete articles, not previously published elsewhere, in the following topics. 

The topics of interest include, but are not limited to:

  • AI-empowered innovative classification techniques and testbeds for healthcare in IoT-cloud platform
  • AI- empowered big data analytics and cognitive computing for smart health monitoring
  • Advanced AIIoT convergent services, systems, infrastructure and techniques for healthcare
  • AI-supported IoT data analytics for smart healthcare
  • Machine learning-based smart homecare for mobile-enabled fall detection of disabled or elderly people
  • AIIoT-empowered data analysis for COVID-19
  • AI-enabled contact tracing for preventing the spread of the COVID-19
  • AI and IoT convergence for pandemic management and monitoring
  • Intelligent IoT-driven diagnosis and prognosis mechanisms for infectious diseases
  • IoT cloud-based predictive analysis for personalized healthcare
  • AI- supported healthcare in IoT-cloud platform
  • AIIoT- supported approaches and testbeds for social distance monitoring in pandemic prevention
  • Security, privacy, and trust of AI-IoT convergent smart healthcare system

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility and downloads of articles.

 

Associate Editor:  M. Shamim Hossain, King Saud University, Saudi Arabia

Guest Editors:

    1. Stefan Goebel, Technical University Darmstadt, Germany
    2. Abdulsalam Yassine, Lakehead University, Canada
    3. Diana P. Tobón, Universidad de Medellín, Colombia
    4. Fakhri Karray, University of Waterloo, Canada

 

Relevant IEEE Access Special Sections:

    1. Deep Learning Algorithms for Internet of Medical Things
    2. Behavioral Biometrics for eHealth and Well-Being
    3. Emerging Deep Learning Theories and Methods for Biomedical Engineering

 

IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:
http://mc.manuscriptcentral.com/ieee-access

 For inquiries regarding this Special Section, please contact: mshossain@ksu.edu.sa.