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Technologies
Special Issues
AI-Enabled Smart Healthcare Systems
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AI-Enabled Smart Healthcare Systems

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Assistive Technologies".

Deadline for manuscript submissions: 30 August 2026 | Viewed by 4372

Special Issue Editors

Dr. Umer Saeed

E-Mail Website
Guest Editor
School of Computer Science and Technology, University of Bedfordshire, University Square, Luton LU1 3JU, UK
Interests: artificial intelligence in healthcare; machine learning for anomaly detection; sensor fault diagnosis in wireless networks; human activity recognition; non-invasive respiratory monitoring
Prof. Dr. Luc de Witte

E-Mail Website
Guest Editor
Center of Expertise Health Innovation, The Hague University of Applied Science, 2521 EN Den Haag, The Netherlands
Interests: assistive technology service delivery; care robotics; digital healthcare solutions; technologies applied to global health challenges
Dr. Haipeng Liu

E-Mail Website
Guest Editor
Centre for Intelligent Healthcare, Coventry University, Coventry CV1 5RW, UK
Interests: computational simulation of the cardiovascular system; AI-assisted diagnosis; medical data analysis; wearable sensors
Special Issues, Collections and Topics in MDPI journals
Dr. Sana Ullah Jan

E-Mail Website
Guest Editor
School of Computing Engineering and the Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UK
Interests: artificial intelligence; machine learning; cyber security; intrusion detection systems; information security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Rapid advancements in artificial intelligence (AI) have revolutionized healthcare by enabling innovative solutions for patient care, diagnostics, and operational efficiency. This Special Issue focuses on exploring cutting-edge AI technologies, methodologies, and systems that address challenges in smart healthcare. We invite contributions that highlight the integration of AI in healthcare, encompassing areas such as predictive analytics, personalized medicine, remote monitoring, decision support systems, and robotics-assisted care. Research addressing the ethical considerations, data privacy, and security in AI-enabled healthcare systems is also welcome. Our aim is to foster an interdisciplinary dialogue to advance the development and adoption of AI solutions for improving healthcare outcomes, operational excellence, and patient experiences.

Dr. Umer Saeed
Prof. Dr. Luc de Witte
Dr. Haipeng Liu
Dr. Sana Ullah Jan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Technologies is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • smart healthcare systems
  • predictive analytics
  • personalized medicine
  • remote monitoring
  • decision support systems
  • robotics in healthcare
  • data privacy in artificial intelligence
  • ethical artificial intelligence in healthcare
  • healthcare automation

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Published Papers (4 papers)

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Research

18 pages, 1921 KB  
Article
Prediction of Sleep Apnea Occurrence from a Single-Lead Electrocardiogram Using Stacking Hybrid Architecture with Gated Recurrent Neural Network Architectures and Logistic Regression
by Tan-Hsu Tan, Guan-Hua Chen, Shing-Hong Liu and Wenxi Chen
Technologies 2026, 14(2), 92; https://doi.org/10.3390/technologies14020092 - 1 Feb 2026
Viewed by 60
Abstract
Obstructive sleep apnea (OSA) is a common sleep disorder that impacts patient health and imposes a burden on families and healthcare systems. The diagnosis of OSA is usually performed through overnight polysomnography (PSG) in a hospital setting. In recent years, OSA detection using [...] Read more.
Obstructive sleep apnea (OSA) is a common sleep disorder that impacts patient health and imposes a burden on families and healthcare systems. The diagnosis of OSA is usually performed through overnight polysomnography (PSG) in a hospital setting. In recent years, OSA detection using a single-lead electrocardiogram (ECG) has been explored. The advantage of this method is that patients can be measured in home environments. Thus, the aim of this study was to predict occurrences of sleep apnea with parameters extracted from previous single-lead ECG measurements. The parameters were the R-R interval (RRI) and R-wave amplitude (RwA). The dataset was the single-lead ECG Apnea-ECG Database, and a stacking hybrid architecture (SHA) including three gated recurrent neural network architectures (GRNNAs) and logistic regression was proposed to improve the accuracy of OSA detection. Three GRNNAs used three different recurrent neural networks: Bidirectional Long Short-Term Memory (BiLSTM), Gated Recurrent Unit (GRU), and Bidirectional GRU (BiGRU). The challenge of this method was in exploring how many minutes of previous RRI and RwA measurements (n minutes) have the best performance in predicting occurrences of sleep apnea in the future (h minutes). The results showed that the SHA under an n of 20 min had the best performance in predicting occurrences of sleep apnea in the following 10 min: the SHA achieved a precision of 95.79%, sensitivity of 94.74%, specificity of 97.48%, F1-score of 95.26%, and accuracy of 96.45%. The proposed SHA was successful in predicting future sleep apnea occurrence with a single-lead ECG. Thus, this approach could be used in the development of wearable sleep monitors for the management of sleep apnea. Full article
(This article belongs to the Special Issue AI-Enabled Smart Healthcare Systems)
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22 pages, 5508 KB  
Article
A Generative AI-Enhanced Robotic Desktop Automation Framework for Multi-System Nephrology Data Entry in Government Healthcare Platforms
by Sumalee Sangamuang, Perasuk Worragin, Kitti Puritat, Phichete Julrode and Kannikar Intawong
Technologies 2025, 13(12), 558; https://doi.org/10.3390/technologies13120558 - 29 Nov 2025
Viewed by 592
Abstract
This study introduces a Generative AI-Enhanced Robotic Data Automation (AI-ERDA) framework designed to improve accuracy, efficiency, and adaptability in healthcare data workflows. Conducted over a two-month, real-world experiment across three government health platforms—one web-based (NHSO) and two PC-based systems (CHi and TRT)—the study [...] Read more.
This study introduces a Generative AI-Enhanced Robotic Data Automation (AI-ERDA) framework designed to improve accuracy, efficiency, and adaptability in healthcare data workflows. Conducted over a two-month, real-world experiment across three government health platforms—one web-based (NHSO) and two PC-based systems (CHi and TRT)—the study compared the performance of AI-ERDA against a conventional RDA system in terms of usability, automation accuracy, and resilience to user interface (UI) changes. Results demonstrated notable improvements in both usability and reliability. The AI-ERDA achieved a mean System Usability Scale (SUS) score of 80, compared with 68 for the traditional RDA, while Field Exact Match Accuracy increased by 1.8 percent in the web system and by 0.2 to 0.3 percent in the PC systems. During actual UI modifications, the AI-ERDA maintained near-perfect accuracy, with rapid self-correction within one day, whereas the baseline RDA required several days of manual reconfiguration and assistance from the development team to resolve issues. These findings indicate that generative and adaptive automation can effectively reduce manual workload, minimize downtime, and maintain high data integrity across heterogeneous systems. By integrating adaptive learning, semantic validation, and human-in-the-loop oversight, the AI-ERDA framework advances sustainable digital transformation and reinforces transparency, trust, and accountability in healthcare data management. Full article
(This article belongs to the Special Issue AI-Enabled Smart Healthcare Systems)
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22 pages, 906 KB  
Article
Optimizing Teleconsultation Scheduling with a Two-Level Approach Based on Reinforcement Learning
by Wenjia Chen and Jinlin Li
Technologies 2025, 13(12), 546; https://doi.org/10.3390/technologies13120546 - 25 Nov 2025
Viewed by 474
Abstract
Using advanced communication and information technologies, teleconsultation can provide high-quality healthcare services to remote areas. To enhance service efficiency, this study develops a two-level dynamic scheduling model for teleconsultation, which prioritizes optimizing service frequency and incorporates downstream room utilization and overtime risk as [...] Read more.
Using advanced communication and information technologies, teleconsultation can provide high-quality healthcare services to remote areas. To enhance service efficiency, this study develops a two-level dynamic scheduling model for teleconsultation, which prioritizes optimizing service frequency and incorporates downstream room utilization and overtime risk as considerations. The first-level model is a data-driven framework that optimizes the frequency by adjusting service start times. Based on the solutions of the first-level model, a second-level model is built to assign teleconsultation rooms to departments with demands and reduce the total overtime risk and and room opening cost. For solving, an integer programming (IP) solver is embedded in a deep reinforcement learning (DRL) approach. A presorting mechanism of interval constraints is proposed to improve the quality of solutions. For verification, actual teleconsultation data are used as samples. The experimental results demonstrate the effectiveness of the proposed two-level model, the embedded solving algorithm, and the interval constraint presorting mechanism. Compared with real schedules, the two-level model can reduce four service scheduling performance criteria, including demand average waiting time, number of services, risk of overtime, and number of rooms used. As a result, the efficiency of teleconsultation is improved to promote its development. Full article
(This article belongs to the Special Issue AI-Enabled Smart Healthcare Systems)
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18 pages, 3219 KB  
Article
Designing Trustworthy AI Systems for PTSD Follow-Up
by María Cazares, Jorge Miño-Ayala, Iván Ortiz and Roberto Andrade
Technologies 2025, 13(8), 361; https://doi.org/10.3390/technologies13080361 - 15 Aug 2025
Viewed by 2305
Abstract
Post-Traumatic Stress Disorder (PTSD) poses complex clinical challenges due to its emotional volatility, contextual sensitivity, and need for personalized care. Conventional AI systems often fall short in therapeutic contexts due to lack of explainability, ethical safeguards, and narrative understanding. We propose a hybrid [...] Read more.
Post-Traumatic Stress Disorder (PTSD) poses complex clinical challenges due to its emotional volatility, contextual sensitivity, and need for personalized care. Conventional AI systems often fall short in therapeutic contexts due to lack of explainability, ethical safeguards, and narrative understanding. We propose a hybrid neuro-symbolic architecture that combines Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), symbolic controllers, and ensemble classifiers to support clinicians in PTSD follow-up. The proposal integrates real-time anonymization, session memory through patient-specific RAG, and a Human-in-the-Loop (HITL) interface. It ensures clinical safety via symbolic logic rules derived from trauma-informed protocols. The proposed architecture enables safe, personalized AI-driven responses by combining statistical language modeling with explicit therapeutic constraints. Through modular integration, it supports affective signal adaptation, longitudinal memory, and ethical traceability. A comparative evaluation against state-of-the-art approaches highlights improvements in contextual alignment, privacy protection, and clinician supervision. Full article
(This article belongs to the Special Issue AI-Enabled Smart Healthcare Systems)
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