AI-Enabled Digital Health Technologies for Patient-Centered Care and Sustainable Systems

A special issue of Informatics (ISSN 2227-9709). This special issue belongs to the section "Health Informatics".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1768

Editors


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Guest Editor
Division of Social & Behavioral Sciences, School of Public Health, The University of Memphis, Memphis, TN 38152, USA
Interests: generative AI; responsible AI; digital health evaluation; human-AI interaction; equity, access, and organizational integration of AI
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Guest Editor
Department of Design and Architecture, Technological and Higher Education Institute of Hong Kong, Hong Kong, China
Interests: mixed reality; AI; digital twin; digital transformation; green material
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Digital health technologies are increasingly central to patient-centered care, enabling more personalized, responsive and data-informed health services. Advances in artificial intelligence, machine learning, digital platforms and connected health systems have expanded the capacity of health organizations to improve care coordination, service navigation and patient engagement. These developments are particularly relevant in urban contexts, where health outcomes are closely intertwined with infrastructure, service density and long-term system sustainability.

Alongside rapid technological innovation, there is a growing need for robust conceptual frameworks and evaluative approaches that can guide the design, implementation and assessment of artificial intelligence-enabled digital health products. Beyond predictive accuracy or technical performance, stakeholders require systematic ways to evaluate how these technologies support patient-centered care, integrate with urban and health infrastructure and contribute to sustainable health systems. Methodological innovation—combining computational approaches with qualitative and mixed-methods research—is essential to advancing this agenda.

This Special Issue seeks interdisciplinary contributions that examine artificial intelligence-enabled and digital health technologies from conceptual, methodological, organizational and systems perspectives, with a focus on patient-centered care and sustainable health and urban systems.

We invite original research articles, reviews, conceptual papers and methodological contributions that address the development, implementation, evaluation and governance of artificial intelligence-enabled digital health technologies. Submissions may draw from health services research, public health, medicine, informatics, management studies, urban studies and related disciplines.

Topics of Interest Include (but are not limited to):

  • AI and machine learning applications for patient-centered care delivery
  • IoT-enabled health systems integrated with urban infrastructure and service ecosystems
  • Digital platforms supporting access to housing, food systems and mental health services
  • Urban health applications of telehealth, mobile health and remote monitoring
  • Sustainable development perspectives on digital health and health system infrastructure
  • Organizational and managerial implications of artificial intelligence-driven digital transformation in health care
  • Explainable, interpretable and responsible use of artificial intelligence and digital technologies
  • Governance, accountability and risk management of artificial intelligence-enabled health systems

Conceptual and Measurement Contributions

The Special Issue explicitly welcomes work that develops or applies:

  • Conceptual frameworks for understanding artificial intelligence-enabled digital health and patient-centered care
  • Theoretical models linking digital health technologies to health system performance and sustainability
  • Evaluation frameworks for artificial intelligence-based health products and platforms
  • Suggested measures and indicators to assess the effectiveness, usability, integration and system-level impact of AI and digital health technologies
  • Frameworks supporting comparative evaluation across technologies, settings, or populations

Methodological Contributions

We particularly encourage submissions that advance or combine innovative methodologies, including:

  • Machine learning and advanced analytics applied to health and social care data
  • Explainable artificial intelligence and model interpretability in clinical and population health contexts
  • Qualitative research, ethnography and fieldwork examining real-world deployment and use of digital health technologies
  • Mixed-methods designs integrating computational, organizational and social science approaches
  • Design science, implementation research and system-level evaluations of digital health interventions

Submissions should clearly articulate their conceptual, methodological, or empirical contribution to patient-centered care and explain the relevance of artificial intelligence-enabled or digital technologies to health system performance, infrastructure, or sustainability.

Authors are encouraged to highlight implications for health organizations, policymakers, technology developers and practitioners.

We look forward to receiving your contributions.

You may choose our Joint Special Issue in Healthcare.

Prof. Dr. Ricky Leung
Dr. Chi Ho Li
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Informatics 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- and IoT-enabled digital health
  • patient-centered care and service integration
  • health system sustainability and resilience
  • digital transformation of health organizations
  • urban health infrastructure and smart cities
  • evaluation and measurement frameworks for digital health
  • governance, accountability and risk management of AI
  • organizational capabilities and implementation processes
  • responsible and explainable AI in health care
  • mixed-methods, design science and implementation research

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Published Papers (3 papers)

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Research

21 pages, 2378 KB  
Article
Optimizing the Mammography AI Pipeline: From Data Filtering to Vision-Language Models
by Egor Ushakov, Sofya Zimina, Arsenii Litvinov, Sofia Senotrusova, Kirill Lukianov, Tigran G. Gevorkyan and Evgeny Karpulevich
Informatics 2026, 13(8), 125; https://doi.org/10.3390/informatics13080125 - 31 Jul 2026
Viewed by 179
Abstract
The performance of AI solutions in mammography is largely determined by data quality, preprocessing methods, and augmentation strategies. However, systematic evaluation of these factors for models trained on aggregated multicenter datasets remains underexplored. This article presents a comparative assessment of the effects of [...] Read more.
The performance of AI solutions in mammography is largely determined by data quality, preprocessing methods, and augmentation strategies. However, systematic evaluation of these factors for models trained on aggregated multicenter datasets remains underexplored. This article presents a comparative assessment of the effects of different stages of the training pipeline on the final diagnostic accuracy. Using a pooled dataset (VinDr-Mammo, INBreast, CMMD, CBIS-DDSM), we evaluated each pipeline step—from filtering to architecture selection (EfficientNet-B3, CLIP). External testing was conducted on the MosMed database. Among the tested preprocessing steps, filtering the darkest 5% of images proved most effective. For EfficientNet-B3, optimal geometric and photometric augmentations increased test AUROC on the prepared MosMed test set from 0.844 to 0.900. Domain-specific pretraining and high resolution yielded the best performance: Mammo-CLIP achieved an AUROC of 0.949 ± 0.012, and EfficientNet-B3 reached 0.934 ± 0.013. Overall, this study developed a standardized pipeline that includes sequential data filtering and harmonization, augmentation optimization, and architecture selection. This approach ensures reliable and reproducible results for automated mammogram classification. Full article
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18 pages, 8774 KB  
Article
Role of Anthropomorphic Design in Social Robots for Aged Care: A Case Study of Pepper
by James R. Sadler, Samina Ansari, Aila Khan, Michael Lwin and Omar Mubin
Informatics 2026, 13(7), 119; https://doi.org/10.3390/informatics13070119 - 22 Jul 2026
Viewed by 293
Abstract
The increasing aging population in Australia necessitates innovative caregiving solutions to address the growing needs of elderly residents. Humanoid robots, with their physical embodiment and human-like attributes, offer a promising technological intervention. This exploratory study investigates the integration of Pepper, a humanoid robot, [...] Read more.
The increasing aging population in Australia necessitates innovative caregiving solutions to address the growing needs of elderly residents. Humanoid robots, with their physical embodiment and human-like attributes, offer a promising technological intervention. This exploratory study investigates the integration of Pepper, a humanoid robot, into aged care facilities, focusing on its potential to meet the needs of older adults and serve as a daily companion, thereby reducing staff workload. The research explores the anthropomorphic features of Pepper, their role in fostering connection and engagement, and the perception and acceptance of the robot as a companion among elderly residents. Findings highlight Pepper’s potential to enhance the quality of care and support in aged care settings while identifying areas for improvement to ensure its successful adoption. Full article
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27 pages, 6384 KB  
Article
A Mobile Application and Hybrid Hospital Information Exchange System to Improve Healthcare Access for Persons with Disabilities in Thailand
by Piya Sirilak, Pisit Maneechot, Paisarn Muneesawang and Yuttana Homket
Informatics 2026, 13(6), 90; https://doi.org/10.3390/informatics13060090 - 16 Jun 2026
Viewed by 740
Abstract
Persons with Disabilities (PWDs) face persistent barriers to healthcare access, welfare services, and timely medical assistance, particularly where hospital information is fragmented across institutions. In Thailand, these challenges are exacerbated by heterogeneous Hospital Information Systems (HISs) across provincial, district, and sub-district hospitals. This [...] Read more.
Persons with Disabilities (PWDs) face persistent barriers to healthcare access, welfare services, and timely medical assistance, particularly where hospital information is fragmented across institutions. In Thailand, these challenges are exacerbated by heterogeneous Hospital Information Systems (HISs) across provincial, district, and sub-district hospitals. This study presents the design, implementation, and evaluation of an integrated mobile application and a hybrid Hospital Information Exchange (HIE) system to enhance healthcare accessibility and service coordination for PWDs. The platform integrates a user-centered mobile application (iOS and Android) with a hybrid data exchange architecture (MedEx Hybrid) combining an application programming interface (API) and Message Queuing Telemetry Transport (MQTT). This enables real-time and on-demand data exchange while accommodating hospitals with limited infrastructure. Key functionalities include disability registration, emergency medical service (1669) integration, appointment management, rights notification, service location mapping, teleconsultation, and peer communication. Deployment across 159 hospitals nationwide demonstrates system scalability and interoperability. The system supports secure access to electronic medical records and enables emergency responders to retrieve patient information during SOS events, improving continuity of care. Findings confirm the feasibility of the proposed system and its potential to support inclusive digital health and national healthcare interoperability. Full article
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