Smart Healthcare: Technologies and Applications, 2nd Edition

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Topic Information

Dear Colleagues,

The aging population has generated a greater demand for high-quality, high-quantity, and easily accessible healthcare services. “Smart healthcare” refers to the utilization of new-generation information technologies in order to achieve personalized, intelligent, and interconnected healthcare services. The connections between smart healthcare systems enable whole-cycle healthcare, expanding the roles of healthcare from diagnosis and treatment to health management, elderly care, and other parts of the life cycle of individuals. However, adequate coordination among smart healthcare systems must be achieved through the application of new-generation technologies, system engineering, and operation management theories. Due to the characteristics of the healthcare industry, each smart healthcare system must offer the utmost in safety, privacy, and efficient operation. The interconnectedness of smart healthcare systems also requires unique solutions within the realm of healthcare data, information, and knowledge. The expansion of the cycle of care further demands innovations not only in disease diagnosis, surgery, and hospital management but also in specialty fields such as digital health, IoMT, pharmaceutical supply chain, medical insurance, etc. In this Special Issue, we welcome submissions of original research and systematic reviews addressing various domains including, but not limited to, the following:

• Smart healthcare data utilization and governance;
• Smart healthcare information exchange and fusion;
• Smart healthcare knowledge inference and recommendation;
• Smart healthcare system engineering;
• Smart healthcare systems operation management;
• Risk management in smart healthcare systems;
• Hospital operation management in the context of smart healthcare;
• Health screening and monitoring in smart healthcare systems;
• Medical training in the context of smart healthcare;
• Telemedicine and virtual reality in smart healthcare;
• Online social media and OHC with smart healthcare;
• Diagnosis decision support in smart healthcare systems;
• Surgery and smart healthcare;
• Smart healthcare-supported rehabilitation;
• Specialty care with smart healthcare;
• Healthcare behavior analysis in the context of smart healthcare;
• Evaluation and factor analysis of smart healthcare;
• Smart healthcare data security management;
• LLM Application in smart healthcare;
• AI agent application in smart healthcare.

Prof. Dr. Gang Kou
Prof. Dr. Shuai Ding
Prof. Dr. Li Luo
Dr. Tian Lu
Prof. Dr. Yogesan Kanagasingam
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400
Healthcare healthcare	2.7	4.7	2013	22.4 Days	CHF 2700
International Journal of Environmental Research and Public Health ijerph	-	8.5	2004	29.5 Days	CHF 2500
Journal of Clinical Medicine jcm	2.9	5.2	2012	18.5 Days	CHF 2600
Journal of Personalized Medicine jpm	-	6.0	2011	25 Days	CHF 2600
Technologies technologies	3.6	8.5	2013	19.1 Days	CHF 1800

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Published Papers (6 papers)

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17 pages, 1737 KB  

Open AccessReview

Photobiomodulation and Wearable Light Therapies: A Bibliometric Analysis of the Scientific Literature (1970–2025)

by Alberto Grossi, Francesca Campoli, Giuseppe Messina, Giuseppe Caminiti, Matteo Vitarelli, Gabriele Morganti, Elvira Padua and Bruno Ruscello

Int. J. Environ. Res. Public Health 2026, 23(5), 610; https://doi.org/10.3390/ijerph23050610 - 5 May 2026

Viewed by 560

Abstract

Background: This study aims to map the temporal evolution of light-based therapies and identify emerging technological trends in wearable photobiomodulation (PBM) devices. Materials and Methods: A bibliometric analysis (1970–2025) was conducted using three major databases: Scopus, PubMed, and Web of Science. The initial [...] Read more.

Background: This study aims to map the temporal evolution of light-based therapies and identify emerging technological trends in wearable photobiomodulation (PBM) devices. Materials and Methods: A bibliometric analysis (1970–2025) was conducted using three major databases: Scopus, PubMed, and Web of Science. The initial dataset, consisting of 117 articles, was processed using the Bibliometrix package in R (version 4.5.0), resulting in a final set of 110 articles. The analysis followed the TALL model (Tracking, Analysis, Layout, and Learning). Results: Scientific production on phototherapeutic devices began in the early 2000s, peaking in 2024, showing a productivity pattern typical of emerging or highly specialized fields. The period 2010–2023 represents a central thematic hub in research. During this time, new light sources (OLED and QLED) enabled the development of flexible, wearable, and implantable photonic devices. In the recent period (2024–2025), light-based therapies are increasingly integrated with network-connected biosensing systems for tissues or accessories, allowing adaptive treatments and remote monitoring. However, these next-generation devices are still undergoing consolidation and scientific maturation. Conclusions: The results highlight the rapid evolution of research on light-based therapies toward more integrated and clinically validated approaches, indicating growing scientific interest in personalized wearable PBM devices. Full article

(This article belongs to the Topic Smart Healthcare: Technologies and Applications, 2nd Edition)

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10 pages, 891 KB  

Open AccessArticle

Monitoring Spontaneous Swallowing After Tracheostomy Using a Neck-Worn Electronic Stethoscope: A Pilot Study

by Shin Matsumoto, Tetsuro Wada, Yukiyo Shimizu, Satoshi Fukuzawa, Yohei Teramoto, Haruna Nakazawa, Yasushi Hada, Kenji Suzuki and Keiji Tabuchi

J. Clin. Med. 2026, 15(8), 2911; https://doi.org/10.3390/jcm15082911 - 11 Apr 2026

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Abstract

Background/Objectives: Reduced spontaneous swallowing frequency (SSF) may reflect dysphagia. In this study, SSF was evaluated using a neck-worn electronic stethoscope (NWES), certified as a medical device in Japan, with artificial intelligence support in patients undergoing tracheostomy. Methods: This single-center observational study [...] Read more.

Background/Objectives: Reduced spontaneous swallowing frequency (SSF) may reflect dysphagia. In this study, SSF was evaluated using a neck-worn electronic stethoscope (NWES), certified as a medical device in Japan, with artificial intelligence support in patients undergoing tracheostomy. Methods: This single-center observational study included tracheotomy patients who underwent swallowing assessment with an NWES between August 2024 and July 2025. Several variables were evaluated, including tracheostomy cannula cuff status and dietary status, assessed using the Functional Oral Intake Scale (FOIS). The Mann–Whitney U-test was applied, with SSF (/min) measured over 10 min using an NWES as the primary objective variable. Furthermore, Spearman’s correlation analysis was performed to examine the relationship between SSF (/min) and the pharyngeal saliva retention grade, which was determined using the Hyodo score during fiberoptic endoscopic evaluation of swallowing (FEES). Results: Eighteen patients who underwent tracheotomies were included in this study. SSF (/min) increased significantly when the tracheostomy cannula cuff was deflated (p = 0.049) and when the feeding status was FOIS ≥ 3 (p = 0.032) or FOIS ≥ 4 (p = 0.014). Spearman’s correlation analysis revealed a negative correlation between SSF (/min) and the pharyngeal saliva retention grade (ρ = −0.68, p = 0.0019). Conclusions: SSF measured with an NWES tended to increase with improved swallowing function, which is consistent with the outcomes of previous reports. The SSF measurement method used in this study may prove to be a useful clinical tool. Full article

(This article belongs to the Topic Smart Healthcare: Technologies and Applications, 2nd Edition)

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20 pages, 847 KB  

Open AccessReview

Intelligent Support for Radiotherapy: A Review of Clinical Applications for Large Language Models

by Juanjuan Fu, Yifan Cheng, Zhaobin Li and Jie Fu

J. Clin. Med. 2026, 15(7), 2531; https://doi.org/10.3390/jcm15072531 - 26 Mar 2026

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Abstract

Background: Radiotherapy (RT) is a core modality for cancer treatment, yet it is plagued by inter-observer variability in target delineation, inefficient manual workflows, and challenges in fusing multi-type clinical data. Large language models (LLMs), with their superior semantic understanding and cross-modal fusion [...] Read more.

Background: Radiotherapy (RT) is a core modality for cancer treatment, yet it is plagued by inter-observer variability in target delineation, inefficient manual workflows, and challenges in fusing multi-type clinical data. Large language models (LLMs), with their superior semantic understanding and cross-modal fusion capabilities present novel solutions to these challenges. Scope: This narrative review provided a comprehensive overview of the current landscape and emerging trends of LLM applications across the entire RT workflow. Findings: LLMs demonstrated substantial clinical utility in key RT domains, including automated target volume delineation (e.g., Medformer, Radformer), dose prediction (e.g., DoseGNN), treatment planning automation (e.g., GPT-Plan), patient education, clinical decision support, medical information extraction, and prognosis assessment. These applications not only have the potential to enhance the accuracy and efficiency of RT but also facilitate the standardization of clinical pathways. However, widespread clinical adoption was impeded by critical limitations, including model hallucinations, insufficient generalizability, and unresolved issues regarding data privacy and ethical governance. Conclusions: LLMs possessed transformative potential to revolutionize radiation oncology. Future endeavors should prioritize technical refinements to mitigate model deficiencies, establish standardized evaluation benchmarks, and develop robust ethical frameworks. These concerted efforts are crucial for translating LLM research into clinical practice and advancing the era of intelligent, precision RT. Full article

(This article belongs to the Topic Smart Healthcare: Technologies and Applications, 2nd Edition)

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17 pages, 633 KB  

Open AccessArticle

Intra- and Inter-Rater Reliability Analysis of MMSE-K and Tablet PC-Based MMSE-K Kit in Patients with Neurologic Disease

by Seung-Ho Choun, Sang-Woo Lee, Yu-Sun Min, Eunhee Park, Jee-Hyun Kim and Tae-Du Jung

Healthcare 2025, 13(23), 3015; https://doi.org/10.3390/healthcare13233015 - 21 Nov 2025

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Abstract

Background: The increasing prevalence of dementia and mild cognitive impairment (MCI) underscores the need for reliable and scalable digital cognitive screening tools. Although several studies have validated smartphone- or tablet-based assessments in community-dwelling older adults, few have examined their reliability in clinical [...] Read more.

Background: The increasing prevalence of dementia and mild cognitive impairment (MCI) underscores the need for reliable and scalable digital cognitive screening tools. Although several studies have validated smartphone- or tablet-based assessments in community-dwelling older adults, few have examined their reliability in clinical populations with neurological disorders. This study aimed to evaluate the intra- and inter-rater reliability and agreement between the traditional paper-based Mini-Mental State Examination-Korean version (MMSE-K) and a tablet PC-based MMSE-K kit in patients with neurologic diseases undergoing rehabilitation. Methods: A total of 32 patients with neurological conditions—including stroke-related, encephalitic, and myelopathic disorders—participated in this study. Two occupational therapists (OT-A and OT-B) independently administered both the paper- and tablet-based MMSE-K versions following standardized digital instructions and fixed response rules. The intra- and inter-rater reliabilities of the tablet version were analyzed using intraclass correlation coefficients (ICCs) with a two-week retest interval, while Bland–Altman plots were used to assess agreement between the paper and tablet scores. Results: The tablet-based MMSE-K showed strong agreement with the paper-based version (r = 0.969, 95% CI 0.936–0.985, p = 1.05 × 10⁻¹⁹). Intra- and inter-rater reliabilities were excellent, with ICCs ranging from 0.89 to 0.98 for domain scores and 0.98 for the total score, and the Bland–Altman plots showing acceptable agreement without systematic bias. Minor variability was observed in the Attention/Calculation and Comprehension/Judgment domains. Conclusions: The tablet PC-based MMSE-K kit provides a standardized, examiner-independent, and reliable alternative to the traditional paper version for assessing cognitive function in patients with neurologic diseases. These findings highlight the tool’s potential for clinical deployment in hospital and rehabilitation settings, bridging the gap between traditional paper assessments and automated digital screening. Future multicenter studies with larger, disease-diverse cohorts are warranted to establish normative data and validate its diagnostic precision for broader clinical use. Full article

(This article belongs to the Topic Smart Healthcare: Technologies and Applications, 2nd Edition)

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39 pages, 5203 KB  

Open AccessTechnical Note

EMR-Chain: Decentralized Electronic Medical Record Exchange System

by Ching-Hsi Tseng, Yu-Heng Hsieh, Heng-Yi Lin and Shyan-Ming Yuan

Technologies 2025, 13(10), 446; https://doi.org/10.3390/technologies13100446 - 1 Oct 2025

Cited by 2 | Viewed by 2614

Abstract

Current systems for exchanging medical records struggle with efficiency and privacy issues. While establishing the Electronic Medical Record Exchange Center (EEC) in 2012 was intended to alleviate these issues, its centralized structure has brought about new attack vectors, such as performance bottlenecks, single [...] Read more.

Current systems for exchanging medical records struggle with efficiency and privacy issues. While establishing the Electronic Medical Record Exchange Center (EEC) in 2012 was intended to alleviate these issues, its centralized structure has brought about new attack vectors, such as performance bottlenecks, single points of failure, and an absence of patient consent over their data. Methods: This paper describes a novel EMR Gateway system that uses blockchain technology to exchange electronic medical records electronically, overcome the limitations of current centralized systems for sharing EMR, and leverage decentralization to enhance resilience, data privacy, and patient autonomy. Our proposed system is built on two interconnected blockchains: a Decentralized Identity Blockchain (DID-Chain) based on Ethereum for managing user identities via smart contracts, and an Electronic Medical Record Blockchain (EMR-Chain) implemented on Hyperledger Fabric to handle medical record indexes and fine-grained access control. To address the dual requirements of cross-platform data exchange and patient privacy, the system was developed based on the Fast Healthcare Interoperability Resources (FHIR) standard, incorporating stringent de-identification protocols. Our system is built using the FHIR standard. Think of it as a common language that lets different healthcare systems talk to each other without confusion. Plus, we are very serious about patient privacy and remove all personal details from the data to keep it confidential. When we tested its performance, the system handled things well. It can take in about 40 transactions every second and pull out data faster, at around 49 per second. To give you some perspective, this is far more than what the average hospital in Taiwan dealt with back in 2018. This shows our system is very solid and more than ready to handle even bigger workloads in the future. Full article

(This article belongs to the Topic Smart Healthcare: Technologies and Applications, 2nd Edition)

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20 pages, 13180 KB  

Open AccessArticle

Multi-Encoding Contrastive Learning for Dual-Stream Self-Supervised 3D Dental Segmentation Network

by Tian Ma, Xiaoyuan Wei, Jiechen Zhai, Ziang Zhang, Yawen Li and Yuancheng Li

Technologies 2025, 13(9), 419; https://doi.org/10.3390/technologies13090419 - 17 Sep 2025

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Abstract

To address the limitation regarding the supervised dataset scale in the semantic recognition of newly distributed types such as wisdom teeth and missing teeth, the multi-encoding contrastive learning for dual-stream self-supervised 3D dental segmentation network (MECSegNet) is proposed. First, a self-supervised encoder pre-training [...] Read more.

To address the limitation regarding the supervised dataset scale in the semantic recognition of newly distributed types such as wisdom teeth and missing teeth, the multi-encoding contrastive learning for dual-stream self-supervised 3D dental segmentation network (MECSegNet) is proposed. First, a self-supervised encoder pre-training framework is designed by integrating 3D mesh feature representation to construct a deep feature encoding network, where the pre-trained encoder learns universal dental feature representations. Then, a multi-contrastive loss function is established to jointly optimize the self-supervised encoder, extracting effective local and global feature representations while incorporating a cross-stream contrastive loss to learn discriminative features from multiple perspectives. Finally, the improved encoder is integrated into a dual-stream network to build a fine-tuning framework for supervised fine-tuning on a small proportion of labeled data. Experimental results show that, with only 20% labeled data, the proposed MECSegNet achieves a 1.3% improvement in accuracy and a 79.81% reduction in computational cost compared to existing self-supervised methods, while maintaining comparable segmentation accuracy and efficiency to high-performance fully supervised methods. Full article

(This article belongs to the Topic Smart Healthcare: Technologies and Applications, 2nd Edition)

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