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TOPICAL COLLECTIONS

Technologies, Applications, and Challenges in Digital Healthcare
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Guest Editor

Junfeng Miao

Junfeng Miao

Henan Normal University

Junfeng Miao earned his bachelor’s degree in computer science and technology from Henan University of Technology in 2011, followed by a master’s degree in computer science and technology from Harbin Engineering University in 2015. He received his PhD from the School of Computer Science and Technology at the University of Science and Technology Beijing in 2024.He is currently a master’s supervisor at the College of Computer and Information Engineering, Henan Normal University. His primary research interests include Healthcare Internet of Things (IoT) applications and methodologies, artificial intelligence, and network security. He also serves as an editorial board member for Innovative Applications of AI and Innovation & Technology Advances, and is a member of the Global Open Share Publishing Academic Board.

Collection Overview

Digital healthcare is transforming healthcare service models through the integration of artificial intelligence, the Internet of Things, cloud computing, big data, mobile health, digital twins, and remote monitoring technologies. These approaches enable real-time data collection, intelligent analysis, and precise decision-making for disease prevention, assisted diagnosis, personalized treatment, chronic disease management, rehabilitation, and long-term care. Digital healthcare has been widely applied in smart hospitals, remote consultation, wearable health monitoring, medical image analysis, electronic health record management, intelligent triage, and personalized health management. These applications improve medical resource utilization, enhance patient experience, and support a transition from passive treatment toward proactive prevention, continuous monitoring, and precise intervention.


Despite these advances, significant challenges remain. Medical data are highly sensitive, making privacy and security across data collection, transmission, storage, and sharing critical concerns. Inconsistent data standards and limited interoperability among medical systems, platforms, and devices also restrict efficient data circulation and collaborative use. In addition, the accuracy, fairness, transparency, and explainability of artificial intelligence algorithms require further improvement to strengthen trust among clinicians, patients, and healthcare institutions.


Research on key technologies for digital healthcare is therefore essential. Important directions include secure and trustworthy data management, privacy-preserving computing, intelligent diagnostic algorithms, edge computing, and cross-platform interoperability. Advancing these areas will support sustainable digital healthcare ecosystems and improve healthcare efficiency, personalized health management, and intelligent allocation of medical resources. This Topical Collection welcomes high-quality original research papers and review articles in digital healthcare and related interdisciplinary fields and technologies worldwide.

Articles

Multi-view Vision Mamba U-Shaped Network Framework for Medical Image Segmentation

Multi-view Vision Mamba U-Shaped Network Framework for Medical Image Segmentation

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2026

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Abstracts

Association and Diagnostic Performance of PCAT Parameters Combined with Multiparametric CMR in Non-obstructive Coronary Artery Disease

jiangming zhu1,

jiani zou*2

1Department of Radiology, General Hospital of Central Theater Command of People’s Liberation Army, Graduate Joint Training Base, School of Medicine, Wuhan University of Science and Technology,

2Department of Radiology, General Hospital of Central Theater Command of People’s Liberation Army

Near-Infrared Spectroscopy for Determination of Paeoniflorin in Paeonia lactiflora Extract of Tongtian Oral Liquid

Ziliang Dong*1

1Chongqing Taiji Industrial<Group>Co., Ltd.