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Artificial Intelligence in Digital Health
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Artificial Intelligence in Digital Health

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 May 2025 | Viewed by 2882

Special Issue Editors

Dr. Patricia Macedo

E-Mail Website
Guest Editor
1. ALGORITMI Research Centre, University of Minho, Guimarães, Portugal
2. Instituto Politécnico de Setúbal, Setúbal, Portugal
Interests: digital health; digital accessibility; collaborative networks
Dr. Ana Inês Oliveira

E-Mail Website
Guest Editor
NOVA School of Sciences and Technology, FCT NOVA and UNINOVA (Centre of Sciences and Technology), Caparica, Portugal
Interests: collaborative networks; cyber-physical systems; intelligent systems; systems-of-systems
Dr. Filipa Ferrada

E-Mail Website
Guest Editor
Department of Electrical and Comnputer Engineering, Nova School of Science and Technology, FCT NOVA Campus de Caparica, 2829-516 Caparica, Portugal
Interests: collaborative networks; information systems; cognitive systems; cyber physical systems; artificial intelligence

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is reshaping digital health, offering new possibilities for healthcare delivery and patient outcomes. In this Special Issue of Applied Sciences, we explore the latest research and challenges at the intersection of AI and digital health.

We explore how AI is transforming healthcare, from personalized treatment strategies to remote patient monitoring, predictive analytics, and patient self-management. Contributions cover various topics:

  • AI-enabled Diagnostic Tools: Advances in AI-driven diagnostic tools encompass tools for interpreting medical images and analyzing the correlation of diverse medical data, including lab results, historical patient data, and medical images, thereby enhancing diagnostic accuracy.
  • Remote Patient Monitoring: Innovations in remote patient monitoring systems with AI capabilities for continuous health surveillance and early warning detection.
  • Predictive Analytics and Prognostic Models: Exploration of AI-driven predictive analytics for forecasting disease progression and optimizing treatment strategies.
  • Healthcare Workflow Optimization: Investigations into AI solutions for streamlining healthcare workflows and improving operational efficiency.
  • Patient Self-Management: Advances in AI-driven tools empowering patients to actively manage their health through personalized interventions, lifestyle recommendations, and treatment adherence support.
  • Ethical and Regulatory Considerations: Reflections on ethical and regulatory challenges related to AI integration in healthcare.

Original work highlighting the latest research and technical development is encouraged, but review papers and comparative studies are also welcome.

Dr. Patricia Macedo
Dr. Ana Inês Oliveira
Dr. Filipa Ferrada
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. 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

  • artificial intelligence
  • digital health
  • user-centered
  • machine learning
  • deep learning
  • remote patient surveillance
  • person-generated health data
  • conversational agents

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

Published Papers (2 papers)

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19 pages, 979 KiB  
Article
A Conversational Agent for Empowering People with Parkinson’s Disease in Exercising Through Motivation and Support
by Patricia Macedo, Rui Neves Madeira, Pedro Albuquerque Santos, Pedro Mota, Beatriz Alves and Carla Mendes Pereira
Appl. Sci. 2025, 15(1), 223; https://doi.org/10.3390/app15010223 - 30 Dec 2024
Viewed by 762
Abstract
Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms. The MoveONParkinson project aims to enhance exercise engagement among people with Parkinson’s Disease (PwPD) in the Portuguese context through the ONParkinson digital platform, which provides mobile and web interfaces. While [...] Read more.
Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms. The MoveONParkinson project aims to enhance exercise engagement among people with Parkinson’s Disease (PwPD) in the Portuguese context through the ONParkinson digital platform, which provides mobile and web interfaces. While the broader MoveONParkinson project has been previously described from a health-focused perspective, this study specifically focuses on the development and integration of an AI-driven conversational agent (CA) for the Portuguese language, called PANDORA, within the mobile interface of the solution to assist and motivate PwPD in their exercise routines. PANDORA (Parkinson Assistant in Natural Dialogue and Oriented by Rules and Assessments), designed based on Self-Determination Theory (SDT), addresses the psychological needs of autonomy, competence, and relatedness. A preliminary study involving 20 PwPD, 10 caregivers, and 5 healthcare professionals informed the design requirements for PANDORA. The development process involved four main phases: (1) Design of the Chatbot’s Motivation Model, (2) Design and implementation of the conversational agent, (3) Technical Performance Evaluation, and (4) User Experience Evaluation. Technical Performance Evaluation, conducted with three physiotherapists, assessed domain coverage, coherence response capacity, and dialog management capacity, achieving 100% accuracy in domain coverage and coherence response capacity and 89% in dialog management capacity. The User Experience Study involved eight PwPD users recruited from Portuguese healthcare units performing predefined tasks, with user satisfaction scores ranging from 4.2 to 4.9 on a five-point Likert scale. The findings indicate that integrating a conversational agent with motivational cues tends to increase patient engagement. However, further studies are required to determine PANDORA’s impact on exercise engagement in PwPD. Full article
(This article belongs to the Special Issue Artificial Intelligence in Digital Health)
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21 pages, 2572 KiB  
Article
Detecting Suicidal Ideations in Online Forums with Textual and Psycholinguistic Features
by Eldar Yeskuatov, Sook-Ling Chua and Lee Kien Foo
Appl. Sci. 2024, 14(21), 9911; https://doi.org/10.3390/app14219911 - 29 Oct 2024
Viewed by 1215
Abstract
Suicide is a global public health problem that takes hundreds of thousands of lives each year. The key to effective suicide prevention is early detection of suicidal ideations and timely intervention. However, several factors hinder traditional suicide risk screening methods. Primarily, the social [...] Read more.
Suicide is a global public health problem that takes hundreds of thousands of lives each year. The key to effective suicide prevention is early detection of suicidal ideations and timely intervention. However, several factors hinder traditional suicide risk screening methods. Primarily, the social stigma associated with suicide presents a challenge to suicidal ideation detection, as existing methods require patients to explicitly communicate their suicidal propensities. In contrast, progressively more at-risk people choose online platforms—such as Reddit—as their preferred avenues for sharing their suicidal experiences and seeking emotional support. As a result, these online platforms have become an unobtrusive source of user-generated textual data that can be used to detect suicidality with supervised machine learning and natural language processing techniques. In this paper, we proposed a suicidal ideation detection approach that combines textual and psycholinguistic features extracted from the Reddit forum. Subsequently, we selected the most informative features using the Boruta algorithm and employed four classifiers: logistic regression, naïve Bayes, support vector machines, and random forest. The naïve Bayes models trained with the combination of term frequency-inverse document frequency (TF-IDF) and National Research Council (NRC) features demonstrated the highest performance, obtaining a F1 score of 70.99%. Our experimental results illustrate that a combination of textual and psycholinguistic features yields better classification performance compared to using those features separately. Full article
(This article belongs to the Special Issue Artificial Intelligence in Digital Health)
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