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Information Technology for Smart Healthcare
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Information Technology for Smart Healthcare

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 5101

Editors

Prof. Dr. Omar El-Gayar

E-Mail Website
Guest Editor
Department of Information Systems, College of Business, Dakota State University, Madison, SD 57042, USA
Interests: artificial intelligence; machine learning; natural language processing; health informatics; smart agriculture; AI-enabled cybersecurity
Dr. Abdullah Wahbeh

E-Mail Website
Guest Editor Assistant
Computing and Security, Slippery Rock University, Slippery Rock, PA 16066, USA
Interests: health informatics; text mining; m_health

Special Issue Information

Dear Colleagues,

Smart healthcare technologies are revolutionizing medical fields. AI-powered chatbots, robotic surgery, and automated workflows streamline healthcare services. AI-driven decision support systems can help clinicians make accurate, data-driven medical decisions. AI-based mental health apps and virtual therapists offer personalized therapy. Big data analytics and machine learning algorithms can help in disease prediction and personalized care, enhance diagnostic accuracy, and recommend the most effective treatment strategies. Smart devices monitor vital signs in real-time, alerting users and healthcare providers to potential health risks for proactive intervention. Telemedicine, remote monitoring, and AI-assisted home healthcare provide accessible healthcare for underserved areas and improve the quality of life and independence for the elderly and patients with chronic diseases.

In this Special Issue, we solicit papers that explore various aspects of smart healthcare, including AI applications that enable more accurate diagnoses and personalized treatments, healthcare informatics with a focus on the utilization of big data analytics, machine learning algorithms, and data visualization. Another example is the use of wearable devices, IoT sensors, and mobile health applications to monitor vital signs and track chronic conditions. The Special Issue also welcomes papers that analyze and interpret healthcare data to inform clinical decision-making, population health management, and healthcare policy.

Prof. Dr. Omar El-Gayar
Guest Editor

Dr. Abdullah Wahbeh
Guest Editor Assistant

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information 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

  • smart health
  • health informatics
  • artificial intelligence
  • wearables
  • mobile health

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

Published Papers (5 papers)

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Research

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29 pages, 1533 KB  
Article
A Clinician-in-the-Loop Framework for Validating and Selecting Synthetic Paediatric Dermatology Images
by Ali Tariq Nagi, Chiara Bellatreccia, Andrea Borghesi, Arianna Dondi, Luca Pierantoni, Daniele Zama, Iria Neri, Marcello Lanari and Roberta Calegari
Information 2026, 17(8), 749; https://doi.org/10.3390/info17080749 - 1 Aug 2026
Viewed by 190
Abstract
Synthetic data are increasingly proposed as a strategy for addressing data scarcity and representation imbalance in medical AI, particularly for paediatric populations and darker skin tones. However, visually plausible synthetic images may still contain clinically implausible features or fairness-relevant inconsistencies that are not [...] Read more.
Synthetic data are increasingly proposed as a strategy for addressing data scarcity and representation imbalance in medical AI, particularly for paediatric populations and darker skin tones. However, visually plausible synthetic images may still contain clinically implausible features or fairness-relevant inconsistencies that are not adequately captured by automatic image-quality metrics. In this study, we present and empirically evaluate a clinician-guided framework for validating and selecting synthetic paediatric dermatology images. The framework combines a clinician-facing evaluation platform with structured assessments of visual realism, mask quality, diagnostic plausibility, confidence, and skin-tone relevance. Four clinicians with complementary expertise in paediatrics and dermatology completed 282 assessments of 93 real and synthetic images. Synthetic images were often rated as visually realistic but showed lower inter-rater agreement and weaker mask-quality assessments than real images. Clinician realism and confidence ratings were then used to divide 30 synthetic images into 18 approved and 12 non-approved images. To assess downstream utility, we compared a real-only ResNet50 classifier with classifiers augmented using all synthetic images, clinician-approved synthetic images, or non-approved synthetic images. Across three patient-level experimental splits, the clinician-approved condition achieved the strongest overall classification performance and the largest gains for the under-represented Dark-Skin subgroup. Because the Dark-Skin subgroup contained only seven patients and the synthetic subsets differed in size and disease composition, these fairness results should be interpreted as exploratory. The present study therefore provides evidence for clinician-guided validation and data curation rather than for a completed iterative generator-retraining process. Future work will evaluate whether clinician feedback can also support repeated generative-model refinement in larger, multi-centre datasets. Full article
(This article belongs to the Special Issue Information Technology for Smart Healthcare)
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16 pages, 618 KB  
Article
Effectiveness of a Telemedicine-Based Intervention for Childhood Obesity Management: A Randomized Controlled Trial
by Naporn Uengarporn, Ratsadakorn Yimsabai Maneewong, Nuttha Piriyapokin, Boonyanurak Nantiwattara, Atcha Pongpitakdamrong and Wichulada Kiattimongkol
Information 2026, 17(4), 359; https://doi.org/10.3390/info17040359 - 9 Apr 2026
Cited by 1 | Viewed by 1024
Abstract
Telemedicine can address access barriers in childhood obesity management by supporting continuity of care and caregiver engagement. This randomized controlled trial compared a telemedicine-based program with guideline-based usual care among 70 children with obesity (aged 5–15 years) and their caregivers, randomized to telemedicine [...] Read more.
Telemedicine can address access barriers in childhood obesity management by supporting continuity of care and caregiver engagement. This randomized controlled trial compared a telemedicine-based program with guideline-based usual care among 70 children with obesity (aged 5–15 years) and their caregivers, randomized to telemedicine (n = 35) or usual care (n = 35) for 6 months. The telemedicine program included online consultations, digital caregiver education, remote monitoring, and secure messaging via the SUTH application integrated with the hospital information system. The control group received standard outpatient care with routine counseling and printed materials; baseline characteristics were similar between groups. Baseline demographic and clinical characteristics were comparable between groups. After 6 months, both groups showed modest reductions in BMI; however, ANCOVA-adjusted analyses indicated no significant between-group difference in post-intervention BMI. Weight-for-height decreased in both groups, with a slightly greater percentage reduction in the telemedicine group. Caregiver satisfaction and knowledge were significantly higher in the telemedicine group at follow-up (all p < 0.01; knowledge p < 0.001). These findings suggest that telemedicine-based care may contribute to modest improvements in anthropometric outcomes while substantially enhancing caregiver knowledge and healthcare service satisfaction, supporting its role as a scalable adjunct in pediatric obesity management. Full article
(This article belongs to the Special Issue Information Technology for Smart Healthcare)
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9 pages, 622 KB  
Article
Adolescents’ Experience with a Conversational Agent for Depression
by Alanna Testerman, Arjun Roshik Bharat, Tyrique Patterson and Eduardo Bunge
Information 2026, 17(2), 204; https://doi.org/10.3390/info17020204 - 16 Feb 2026
Viewed by 991
Abstract
Conversational Agents have been showing promise for depression in adults in the short-term. Although, there has been little research done for conversational agents (CAs) with depression in adolescents. This study aimed to determine adolescents’ user experience with Athenabot, a behavioral activation CA for [...] Read more.
Conversational Agents have been showing promise for depression in adults in the short-term. Although, there has been little research done for conversational agents (CAs) with depression in adolescents. This study aimed to determine adolescents’ user experience with Athenabot, a behavioral activation CA for depression. The study included 66 participants who interacted with Athenabot. Participants were aged 13 to 18 (mean = 14.12) and predominantly identified as female (56.1%). Participants’ confidence in the CA’s utility to improve mood significantly increased from baseline to post-intervention (p < 0.001). Adolescents provided an acceptable Net Promoter Score of 6.73. Positive themes from feedback included the CA being helpful and favorably viewed, while negative themes included its perceived audience-dependency and impersonal nature. Recommendations for improvement included reducing repetitive questions and enhancing personalization. Adolescents significantly preferred multiple-choice questions over typed response questions (p < 0.05). However, there were no significant differences in preference for emojis, memes, or GIFs. Adolescents reported an increased confidence that the CA could improve their mood. While the CAs received acceptable support, feedback highlighted a need for improved engagement and personalization. Adolescents favored multiple-choice button questions over typed responses and preferred GIFs over memes and emojis, with no significant demographic differences. Full article
(This article belongs to the Special Issue Information Technology for Smart Healthcare)
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20 pages, 546 KB  
Article
Provider Perspectives on Sociotechnical Alignment of Intelligent Clinical Decision Support Systems
by Andy Behrens, Cherie Noteboom and Patti Brooks
Information 2026, 17(2), 191; https://doi.org/10.3390/info17020191 - 13 Feb 2026
Cited by 1 | Viewed by 1167
Abstract
Intelligent Clinical Decision Support Systems (ICDSS) are increasingly integrated into healthcare settings to enhance clinical decision-making, efficiency, and patient safety. Despite advances in artificial intelligence-enabled decision support, ICDSS adoption remains inconsistent, particularly in complex clinical environments where professional autonomy, workflow alignment, and accountability [...] Read more.
Intelligent Clinical Decision Support Systems (ICDSS) are increasingly integrated into healthcare settings to enhance clinical decision-making, efficiency, and patient safety. Despite advances in artificial intelligence-enabled decision support, ICDSS adoption remains inconsistent, particularly in complex clinical environments where professional autonomy, workflow alignment, and accountability are critical. This study examines healthcare providers’ perspectives on ICDSS through a grounded theory approach informed by established Information Systems theories, including the Unified Theory of Acceptance and Use of Technology (UTAUT), Technology Acceptance Model (TAM), Diffusion of Innovation (DOI), and the Human-Organization-Technology fit (HOT-fit) framework. Semi-structured interviews were conducted with 11 providers within a large, integrated healthcare organization, and data were analyzed using open, axial, and selective coding. The findings reveal three interrelated dimensions shaping ICDSS use: provider experience, clinical utility, and adaptation. While ICDSS were perceived as valuable for improving efficiency, supporting treatment decisions, and enhancing patient safety, their adoption was constrained by cognitive overload, workflow misalignment, data quality concerns, and perceived threats to professional autonomy. Trust, explainability, and workflow fit emerged as central mechanisms influencing selective use rather than full adoption. By grounding provider perspectives within a sociotechnical lens, this study extends existing IS theories to the context of AI-enabled clinical decision support and offers empirically grounded insights for designing ICDSS that better align with clinical practice. Full article
(This article belongs to the Special Issue Information Technology for Smart Healthcare)
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Review

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30 pages, 2287 KB  
Review
Exploring the Application of Information and Communication Technologies in Age-Friendly Healthcare: A Systematic Scoping Review
by Jiahao Li, Yilin Zhai and Jun Ma
Information 2026, 17(6), 520; https://doi.org/10.3390/info17060520 - 23 May 2026
Viewed by 502
Abstract
The rapidly aging global population is placing immense pressure on healthcare systems, which are struggling to meet the needs of older adults. Information and communication technologies (ICTs) are considered a key driver in supporting the development of age-friendly healthcare models. This scoping review [...] Read more.
The rapidly aging global population is placing immense pressure on healthcare systems, which are struggling to meet the needs of older adults. Information and communication technologies (ICTs) are considered a key driver in supporting the development of age-friendly healthcare models. This scoping review aims to map and structure the multifaceted applications of ICTs in age-friendly healthcare, focusing on their design, benefits, challenges, and implementation in different contexts. We followed the PRISMA-ScR guidelines and conducted a systematic search of five major databases (Web of Science, Scopus, PubMed, ScienceDirect, and IEEE Xplore), supplemented with backward citation chaining to improve the robustness of literature identification. The results show that ICTs can help older adults by improving their access to healthcare information, enhancing their care coordination, supporting their independent living, and personalizing their health management. Key challenges include user experience issues for older adults, data privacy and security concerns, and implementation barriers related to resources and professional support. Effective implementation of ICTs requires greater emphasis on age-centered design, robust data governance, and scalable integration with existing healthcare systems. We further propose a Technology Design–Scenario Application–Effect Evaluation (TD-SA-EE) analytical framework for ICT application in age-friendly healthcare; the framework is grounded in sociotechnical systems theory to provide explanatory insights beyond descriptive classification. This research provides insights into optimizing age-friendly healthcare through ICTs and contributes to fully leveraging ICTs in building sustainable and equitable age-friendly healthcare systems. Full article
(This article belongs to the Special Issue Information Technology for Smart Healthcare)
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