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Future Internet
Special Issues
Artificial Intelligence-Enabled Smart Healthcare
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Artificial Intelligence-Enabled Smart Healthcare

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Smart System Infrastructure and Applications".

Deadline for manuscript submissions: 20 May 2025 | Viewed by 3157

Special Issue Editors

Dr. Lidia Bajenaru

E-Mail Website
Guest Editor
1. Department of Communications, Applications, and Digital System, National Institute for Research and Development in Informatics—ICI Bucharest, 011455 Bucharest, Romania
2. Department of Computer Science, Faculty of Automatic Control and Computers, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania
Interests: semantic-based systems; e-learning; e-health; ambient assisted living; ontology; computer science
Dr. Virginia Sandulescu

E-Mail Website
Guest Editor
Department of Communications, Applications, and Digital System, National Institute for Research and Development in Informatics—ICI Bucharest, 011455 Bucharest, Romania
Interests: affective computing; e-health; m-health; ambient intelligence; domotic systems; building management systems

Special Issue Information

Dear Colleagues,

Rapid advances in artificial intelligence (AI) are significantly transforming the healthcare sector, from improving diagnostic accuracy to facilitating personalized treatment and care. This Special Issue of Future Internet aims to explore how AI is shaping the future of healthcare, particularly through its integration into intelligent systems that improve patient outcomes, streamline healthcare processes, and enable effective remote monitoring and telemedicine.

We invite researchers, practitioners, and healthcare professionals to contribute original research papers, review articles, and case studies examining AI's development and application in healthcare. This Special Issue aims to address both the opportunities and challenges of implementing AI technologies, considering the technical, ethical, and regulatory dimensions. Ideally, submissions should focus on novel AI methodologies, real-world applications, or interdisciplinary approaches that highlight how AI can be effectively leveraged to build smarter health systems.

This Special Issue will serve as a platform for researchers, practitioners, and healthcare professionals to share their findings, discuss cutting-edge innovations, and propose solutions to real-world challenges in AI-enabled healthcare.

We warmly welcome innovative research and solutions that address the pressing challenges and cutting-edge advancements in AI for healthcare. Contributions should focus on how AI technologies are reshaping healthcare systems, enhancing patient care, and driving efficiency. Topics of interest include, but are not limited to, the following:

  • AI-driven Diagnostic Tools and Systems;
  • Personalized and Predictive Healthcare;
  • AI in Remote Monitoring and Telehealth;
  • Privacy and Security in AI Healthcare Applications;
  • AI for Healthcare Workflow Optimization;
  • Digital Twins in Healthcare;
  • AI in Mental Health and Wellbeing;
  • AI for Population Health and Public Health.

Dr. Lidia Bajenaru
Dr. Virginia Sandulescu
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Future Internet 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 1600 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 (AI) in healthcare
  • smart healthcare systems
  • machine learning in medicine
  • AI-powered diagnostics
  • predictive analytics in healthcare
  • remote monitoring
  • telemedicine
  • digital twins in healthcare
  • ai-driven workflow optimization
  • wearable health technologies
  • AI and mental health
  • healthcare IoT (Internet of Things)
  • data privacy and security in healthcare

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

17 pages, 1488 KiB  
Article
A Machine Learning Approach for Predicting Maternal Health Risks in Lower-Middle-Income Countries Using Sparse Data and Vital Signs
by Avnish Malde, Vishnunarayan Girishan Prabhu, Dishant Banga, Michael Hsieh, Chaithanya Renduchintala and Ronald Pirrallo
Future Internet 2025, 17(5), 190; https://doi.org/10.3390/fi17050190 - 22 Apr 2025
Viewed by 222
Abstract
According to the World Health Organization, maternal mortality rates remain a critical public health issue, with 94% of maternal deaths occurring in low- and middle-income countries (LMICs), where the rates reached 430 per 100,000 live births in 2020 compared to 13 in high-income [...] Read more.
According to the World Health Organization, maternal mortality rates remain a critical public health issue, with 94% of maternal deaths occurring in low- and middle-income countries (LMICs), where the rates reached 430 per 100,000 live births in 2020 compared to 13 in high-income countries. Despite this difference, only a few studies have investigated whether sparse data and features such as vital signs can effectively predict maternal health risks. This study addresses this gap by evaluating the predictive capability of vital sign data using machine learning models trained on a dataset of 1014 pregnant women from rural Bangladesh. This study developed multiple machine learning models using a dataset containing age, blood pressure, temperature, heart rate, and blood glucose of 1014 pregnant women from rural Bangladesh. The models’ performance were evaluated using regular, random and stratified sampling techniques. Additionally, we developed a stacking ensemble machine learning model combining multiple methods to evaluate predictive accuracy. A key contribution of this study is developing a stacking ensemble model combined with stratified sampling, an approach not previously considered in maternal health risk prediction. The ensemble model using stratified sampling achieved the highest accuracy (87.2%), outperforming CatBoost (84.7%), XGBoost (84.2%), random forest (81.3%) and decision trees (80.3%) without stratified sampling. Observations from our study demonstrate the feasibility of using sparse data and features for maternal health risk prediction using algorithms. By focusing on data from resource-constrained settings, we show that machine learning offers a convenient and accessible solution to improve prenatal care and reduce maternal deaths in LMICs. Full article
(This article belongs to the Special Issue Artificial Intelligence-Enabled Smart Healthcare)
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22 pages, 2235 KiB  
Article
Multimodal Fall Detection Using Spatial–Temporal Attention and Bi-LSTM-Based Feature Fusion
by Jungpil Shin, Abu Saleh Musa Miah, Rei Egawa, Najmul Hassan, Koki Hirooka and Yoichi Tomioka
Future Internet 2025, 17(4), 173; https://doi.org/10.3390/fi17040173 - 15 Apr 2025
Viewed by 596
Abstract
Human fall detection is a significant healthcare concern, particularly among the elderly, due to its links to muscle weakness, cardiovascular issues, and locomotive syndrome. Accurate fall detection is crucial for timely intervention and injury prevention, which has led many researchers to work on [...] Read more.
Human fall detection is a significant healthcare concern, particularly among the elderly, due to its links to muscle weakness, cardiovascular issues, and locomotive syndrome. Accurate fall detection is crucial for timely intervention and injury prevention, which has led many researchers to work on developing effective detection systems. However, existing unimodal systems that rely solely on skeleton or sensor data face challenges such as poor robustness, computational inefficiency, and sensitivity to environmental conditions. While some multimodal approaches have been proposed, they often struggle to capture long-range dependencies effectively. In order to address these challenges, we propose a multimodal fall detection framework that integrates skeleton and sensor data. The system uses a Graph-based Spatial-Temporal Convolutional and Attention Neural Network (GSTCAN) to capture spatial and temporal relationships from skeleton and motion data information in stream-1, while a Bi-LSTM with Channel Attention (CA) processes sensor data in stream-2, extracting both spatial and temporal features. The GSTCAN model uses AlphaPose for skeleton extraction, calculates motion between consecutive frames, and applies a graph convolutional network (GCN) with a CA mechanism to focus on relevant features while suppressing noise. In parallel, the Bi-LSTM with CA processes inertial signals, with Bi-LSTM capturing long-range temporal dependencies and CA refining feature representations. The features from both branches are fused and passed through a fully connected layer for classification, providing a comprehensive understanding of human motion. The proposed system was evaluated on the Fall Up and UR Fall datasets, achieving a classification accuracy of 99.09% and 99.32%, respectively, surpassing existing methods. This robust and efficient system demonstrates strong potential for accurate fall detection and continuous healthcare monitoring. Full article
(This article belongs to the Special Issue Artificial Intelligence-Enabled Smart Healthcare)
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20 pages, 2278 KiB  
Article
Enhanced Fall Detection Using YOLOv7-W6-Pose for Real-Time Elderly Monitoring
by Eugenia Tîrziu, Ana-Mihaela Vasilevschi, Adriana Alexandru and Eleonora Tudora
Future Internet 2024, 16(12), 472; https://doi.org/10.3390/fi16120472 - 19 Dec 2024
Cited by 1 | Viewed by 1949
Abstract
This study aims to enhance elderly fall detection systems by using the YOLO (You Only Look Once) object detection algorithm with pose estimation, improving both accuracy and efficiency. Utilizing YOLOv7-W6-Pose’s robust real-time object detection and pose estimation capabilities, the proposed system can effectively [...] Read more.
This study aims to enhance elderly fall detection systems by using the YOLO (You Only Look Once) object detection algorithm with pose estimation, improving both accuracy and efficiency. Utilizing YOLOv7-W6-Pose’s robust real-time object detection and pose estimation capabilities, the proposed system can effectively identify falls in video feeds by using a webcam and process them in real-time on a high-performance computer equipped with a GPU to accelerate object detection and pose estimation algorithms. YOLO’s single-stage detection mechanism enables quick processing and analysis of video frames, while pose estimation refines this process by analyzing body positions and movements to accurately distinguish falls from other activities. Initial validation was conducted using several free videos sourced online, depicting various types of falls. To ensure real-time applicability, additional tests were conducted with videos recorded live using a webcam, simulating dynamic and unpredictable conditions. The experimental results demonstrate significant advancements in detection accuracy and robustness compared to traditional methods. Furthermore, the approach ensures data privacy by processing only skeletal points derived from pose estimation, with no personal data stored. This approach, integrated into the NeuroPredict platform developed by our team, advances fall detection technology, supporting better care and safety for older adults. Full article
(This article belongs to the Special Issue Artificial Intelligence-Enabled Smart Healthcare)
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