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Advances in Digital Health Technologies

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 October 2026 | Viewed by 2453

Special Issue Editors

Special Issue Information

Dear Colleagues,

The rapid advancement of digital technologies is transforming healthcare systems. The integration of artificial intelligence, wearable devices, and interconnected medical technologies is reshaping how health data is collected, analyzed, and utilized. Key themes include the enhancement of diagnostic accuracy, the personalization of treatment plans, and the facilitation of remote patient monitoring. Nevertheless, this evolution is also associated with complex challenges related to privacy, safety and security; ensuring transparency and explainability in systems; and upholding the core principles of fairness, responsibility, accountability, and trust. To fully harness the potential of digital health, it is essential to deepen our understanding of how these technologies interact with clinical practice and patient outcomes, supported by rigorous scientific research and ethical frameworks.

This Special Issue seeks high-quality original papers that focus on recent advances in digital health technologies, emphasizing their development, implementation, and impact on healthcare delivery. We welcome contributions that explore novel AI applications, IoMT systems, wearable innovations, data security solutions, and ethical or regulatory considerations.

The scope of this Special Issue includes, but is not limited to, the following topics:

  • Artificial Intelligence and Machine Learning in healthcare diagnostics and treatment planning;
  • Internet of Medical Things (IoMT) and sensor networks for patient monitoring;
  • Wearable health devices and real-time data acquisition;
  • Data security, privacy protection, and cybersecurity in digital health;
  • Integration of digital health tools in clinical workflows and telemedicine;
  • Personalized medicine and digital therapeutics enabled by technology;
  • Big data analytics, interoperability, and standards for health information exchange;
  • Human–AI collaboration and trust in automated healthcare systems. 

Dr. Lourdes Martínez-Villaseñor
Prof. Dr. Hiram Ponce
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • digital health
  • artificial intelligence (AI)
  • Machine Learning,Internet of Medical Things (IoMT)
  • wearable health devices
  • patient monitoring
  • data privacy
  • cybersecurity
  • explainable AI (XAI)
  • transparency in AI
  • health equity
  • ethical AI
  • personalized medicine
  • digital therapeutics
  • clinical decision support
  • health data analytics
  • interoperability
  • regulatory compliance
  • telemedicine
  • human–AI interaction
  • trustworthy AI
  • healthcare innovation
  • big data in healthcare
  • responsible AI
  • healthcare technology integration

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Published Papers (2 papers)

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Review

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21 pages, 804 KB  
Review
A Focused Survey of Generative AI-Based Music Therapy Systems: Recent Progress and Open Challenges
by Jin S. Seo
Appl. Sci. 2026, 16(9), 4120; https://doi.org/10.3390/app16094120 - 23 Apr 2026
Viewed by 284
Abstract
Generative artificial intelligence (AI)-based music generation has the potential to create new opportunities for music therapy; however, integrated examinations of generative AI and music therapy remain limited. This paper provides a focused survey of recent studies that apply generative AI within music therapy-related [...] Read more.
Generative artificial intelligence (AI)-based music generation has the potential to create new opportunities for music therapy; however, integrated examinations of generative AI and music therapy remain limited. This paper provides a focused survey of recent studies that apply generative AI within music therapy-related contexts, examining how such approaches have been explored in relation to therapeutic considerations, including emotional and physiological regulation. Rather than offering an exhaustive historical review, we analyze generative AI-augmented music therapy systems from a system-level perspective, focusing on their overall design and implementation. Based on this survey, we discuss open research challenges at the intersection of generative music, adaptive systems, and digital health, and outline future research directions toward scalable and personalized generative AI-based music therapy. Full article
(This article belongs to the Special Issue Advances in Digital Health Technologies)
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Other

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36 pages, 473 KB  
Systematic Review
Artificial Intelligence and Deep Learning-Based Methods and Devices for Measuring Vital Signs: A Systematic Review
by César Castrejón-Peralta, Jesús Yaljá Montiel-Pérez, Saulo Abraham Gante-Díaz, Jonathan Axel Cruz-Vazquez, Abel Alejandro Rubín-Alvarado, Zayra Reyes-Vera, Juan Manuel Torres-Delgadillo, Juan Humberto Sossa-Azuela, Osslan Osiris Vergara-Villegas and Vianey Guadalupe Cruz-Sánchez
Appl. Sci. 2026, 16(2), 1126; https://doi.org/10.3390/app16021126 - 22 Jan 2026
Cited by 1 | Viewed by 1082
Abstract
Measuring vital signs can reveal the state of body functioning and help to detect a health problem. In the state-of-the-art, numerous methods and devices are available for measuring vital signs. However, with the advent of artificial intelligence, new methods have been proposed that [...] Read more.
Measuring vital signs can reveal the state of body functioning and help to detect a health problem. In the state-of-the-art, numerous methods and devices are available for measuring vital signs. However, with the advent of artificial intelligence, new methods have been proposed that employ this technology. This paper aims to highlight the recent methods and devices based on artificial intelligence and novel techniques for measuring vital signs and processing algorithms. We analyzed 122 papers and classified them into six categories: (i) body temperature, (ii) blood oxygen saturation, (iii) heart rate monitoring, (iv) respiratory rate, (v) blood pressure, and (vi) simultaneous vital sign measurements. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology was used for the search and selection of scientific papers. The criteria to guide the scope of the review were defined with the Population, Intervention, Comparison, Outcomes, and Context (PICOC) methodology. The review highlighted significant efforts to develop and implement contactless, non-invasive devices for continuous monitoring outside clinical environments. It also revealed clear pathways for integrating AI at different stages of measurement and signal processing methods. Full article
(This article belongs to the Special Issue Advances in Digital Health Technologies)
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