Special lssues

Artificial Intelligence enabled Smart Health Care Decision Support Systems

Submission Deadline: 15 August 2022 (closed)

Guest Editors

Dr. Kottilingam Kottursamy, SRM Institute of Science and Technology, India.
Dr. Keping Yu, Waseda University, Japan.
Dr. Sheeba Backia Mary Baskaran, Lenovo, Motorola Mobility Germany, Germany.

Summary

Artificial intelligence and Internet-of-Things (IoT) have taken over all aspects of human life, and its applications vary widely from basic health monitoring to remote health informatics. From a lack of access to basic healthcare services in many places around the world to a general staffing shortage that's expected to reach 18 million by 2030. 21st-century healthcare professionals are confronted by many technological advancements and large amounts of data. Physicians and nurses are overwhelmed by data from infusion pumps, vital sign monitors, laboratory tests, molecular tests, medical images and all the data that has been recorded in electronic medical records. Deep learning is gaining momentum in the various medical imaging and information analysis fields, while there has been a large volume on the use of deep learning for Covid detection, most of them are based on Convolutional Neural Networks (CNNs). CNN, albeit powerful, lacks a global understanding of images because of its image-specific inductive biases. There are plenty of gaps that technology like 5G, cloud, and AI are primed to offset. Hence, smart healthcare becomes very important.

 

Objective

The different medical electronic devices, healthcare monitoring sensors, diagnostic and imaging devices can be viewed as smart devices or objects constituting a core part of the AI-based smart healthcare systems. AI integrated IoT-based healthcare services are expected to reduce costs, increase the quality of life, and enrich the user’s experience. This special issue focuses on original and unpublished articles in the area of Cognitive health informatics, smart healthcare solutions, remote drug behavioral analysis, data science-based drug recommendation systems, Inventive smart health systems, healthcare embedded smart homes, and other broad domains of healthcare decision support systems.

 

The topics to be covered within this issue are listed below:

1. AI-enabled Healthcare information systems

2. Clinical information systems

3. Decision support systems

4. Medical and biological imaging informatics

5. Wearable systems

6. Body area/sensor networks 

7. Informatics in biological and physiological systems

8. Personalized and pervasive health technologies (u-, p-, m- and e-Health),

9. AI and Telemedicine

10. Applications for home healthcare and wellness management.

11. Ambient assisted living

12. The internet of m-health things (m-IoT)

13. Adverse drug reaction

14. Blockchain for healthcare

15. Pediatric Information systems

16. Wearable device access

17. Smart systems for glucose level sensing

18 AI for Non-Invasive healthcare information solutions.


Keywords

Artificial Intelligence, Smart healthcare, IoT, Data science, Machine learning

Published Papers


  • Open Access

    ARTICLE

    Movement Function Assessment Based on Human Pose Estimation from Multi-View

    Lingling Chen, Tong Liu, Zhuo Gong, Ding Wang
    Computer Systems Science and Engineering, Vol.48, No.2, pp. 321-339, 2024, DOI:10.32604/csse.2023.037865
    (This article belongs to this Special Issue: Artificial Intelligence enabled Smart Health Care Decision Support Systems)
    Abstract Human pose estimation is a basic and critical task in the field of computer vision that involves determining the position (or spatial coordinates) of the joints of the human body in a given image or video. It is widely used in motion analysis, medical evaluation, and behavior monitoring. In this paper, the authors propose a method for multi-view human pose estimation. Two image sensors were placed orthogonally with respect to each other to capture the pose of the subject as they moved, and this yielded accurate and comprehensive results of three-dimensional (3D) motion reconstruction that helped capture their multi-directional poses.… More >

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