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Ehealth, Telemedicine, and AI in the Precision Medicine Era
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Ehealth, Telemedicine, and AI in the Precision Medicine Era

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Methodology, Drug and Device Discovery".

Deadline for manuscript submissions: 20 November 2025 | Viewed by 10624

Special Issue Editor

Dr. Mirella Veras

E-Mail Website
Guest Editor
Department of Physical Therapy, College of Rehabilitation Sciences, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0T6, Canada
Interests: applied artificial intelligence; aging; global health; virtual reality; health equity; digital health

Special Issue Information

Dear Colleagues,

Topic brief introduction: We are pleased to introduce a Special Issue focusing on eHealth, telemedicine, AI, and rehabilitation in the precision medicine era. This Special Issue explores how these technologies intersect to optimize healthcare delivery and enhance patient outcomes.

Background: The integration of eHealth, telemedicine, AI, and rehabilitation represents a major advancement in healthcare, enhancing treatment personalization and patient care through digital technologies. Now, these technologies promise to further enhance precision medicine with personalized treatments and proactive care.

Aim and Scope: This Special Issue explores the applications of eHealth, telemedicine, AI, and telerehabilitation in advancing precision medicine. It aims to optimize diagnosis, treatment strategies, and patient management through innovative technological solutions.

Types of Papers Solicited: Original research articles and reviews that explore the integration of digital health technologies in personalized medicine and rehabilitation.

This Special Issue will feature papers on the integration, evaluation, and practical application of eHealth and AI in medicine and rehabilitation. It aims to provide a platform for sharing rapid advancements in AI, precision medicine, wearable devices, remote monitoring, eHealth applications, telemedicine, telerehabilitation, and virtual conferencing tools. Themes include, but are not limited to, innovative assessment and screening methods, AI-driven treatment planning and monitoring, personalized intervention strategies, and the use of LLMs (Large Language Models) in healthcare. Throughout this Special Issue, I look forward to facilitating active discussion on this topic.

Dr. Mirella Veras
Guest Editor

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. Journal of Personalized Medicine 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 2600 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
  • telemedicine
  • telerehabilitation
  • E-health
  • precision medicine
  • remote monitoring
  • wearable devices
  • mobile health (mHealth)
  • virtual health
  • health informatics
  • machine learning
  • predictive analytics
  • personalized medicine
  • health technology

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • 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

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11 pages, 913 KiB  
Article
Artificial Intelligence-Driven Analysis of Telehealth Effectiveness in Youth Mental Health Services: Insights from SAMHSA Data
by Masab Mansoor and Kashif Ansari
J. Pers. Med. 2025, 15(2), 63; https://doi.org/10.3390/jpm15020063 - 7 Feb 2025
Viewed by 1543
Abstract
Background: The rapid adoption of telehealth services for youth mental health care necessitates a comprehensive evaluation of its effectiveness. This study aimed to analyze the impact of telehealth on youth mental health outcomes using artificial intelligence techniques applied to large-scale public health [...] Read more.
Background: The rapid adoption of telehealth services for youth mental health care necessitates a comprehensive evaluation of its effectiveness. This study aimed to analyze the impact of telehealth on youth mental health outcomes using artificial intelligence techniques applied to large-scale public health data. Methods: We conducted an AI-driven analysis of data from the National Survey on Drug Use and Health (NSDUH) and other SAMHSA datasets. Machine learning techniques, including random forest models, K-means clustering, and time series analysis, were employed to evaluate telehealth adoption patterns, predictors of effectiveness, and comparative outcomes with traditional in-person care. Natural language processing was used to analyze sentiment in user feedback. Results: Telehealth adoption among youth increased significantly, with usage rising from 2.3 sessions per year in 2019 to 8.7 in 2022. Telehealth showed comparable effectiveness to in-person care for depressive disorders and superior effectiveness for anxiety disorders. Session frequency, age, and prior diagnosis were identified as key predictors of telehealth effectiveness. Four distinct user clusters were identified, with socioeconomic status and home environment strongly associated with positive outcomes. States with favorable reimbursement policies saw a 15% greater increase in youth telehealth utilization and 7% greater improvement in mental health outcomes. Conclusions: Telehealth demonstrates significant potential in improving access to and effectiveness of mental health services for youth. However, addressing technological barriers and socioeconomic disparities is crucial to maximize its benefits. Full article
(This article belongs to the Special Issue Ehealth, Telemedicine, and AI in the Precision Medicine Era)
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11 pages, 931 KiB  
Article
Early Detection of Mental Health Crises through Artificial-Intelligence-Powered Social Media Analysis: A Prospective Observational Study
by Masab A. Mansoor and Kashif H. Ansari
J. Pers. Med. 2024, 14(9), 958; https://doi.org/10.3390/jpm14090958 - 9 Sep 2024
Cited by 2 | Viewed by 6245
Abstract
Background: The early detection of mental health crises is crucial for timely interventions and improved outcomes. This study explores the potential of artificial intelligence (AI) in analyzing social media data to identify early signs of mental health crises. Methods: We developed a multimodal [...] Read more.
Background: The early detection of mental health crises is crucial for timely interventions and improved outcomes. This study explores the potential of artificial intelligence (AI) in analyzing social media data to identify early signs of mental health crises. Methods: We developed a multimodal deep learning model integrating natural language processing and temporal analysis techniques. The model was trained on a diverse dataset of 996,452 social media posts in multiple languages (English, Spanish, Mandarin, and Arabic) collected from Twitter, Reddit, and Facebook over 12 months. Its performance was evaluated using standard metrics and validated against expert psychiatric assessments. Results: The AI model demonstrated a high level of accuracy (89.3%) in detecting early signs of mental health crises, with an average lead time of 7.2 days before human expert identification. Performance was consistent across languages (F1 scores: 0.827–0.872) and platforms (F1 scores: 0.839–0.863). Key digital markers included linguistic patterns, behavioral changes, and temporal trends. The model showed varying levels of accuracy for different crisis types: depressive episodes (91.2%), manic episodes (88.7%), suicidal ideation (93.5%), and anxiety crises (87.3%). Conclusions: AI-powered analysis of social media data shows promise for the early detection of mental health crises across diverse linguistic and cultural contexts. However, ethical challenges, including privacy concerns, potential stigmatization, and cultural biases, need careful consideration. Future research should focus on longitudinal outcome studies, ethical integration of the method with existing mental health services, and developing personalized, culturally sensitive models. Full article
(This article belongs to the Special Issue Ehealth, Telemedicine, and AI in the Precision Medicine Era)
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Other

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21 pages, 985 KiB  
Study Protocol
A Protocol for AI-Powered Tools to Enhance Mobility and Function in Older Adults: An Evidence and Gap Map
by Mirella Veras, Jordi Pardo, Mê-Linh Lê, Cindy Jussup, José Carlos Tatmatsu-Rocha and Vivian Welch
J. Pers. Med. 2025, 15(1), 29; https://doi.org/10.3390/jpm15010029 - 14 Jan 2025
Viewed by 1893
Abstract
Introduction: Artificial intelligence (AI) is transforming healthcare by enhancing diagnostic accuracy, treatment, and patient monitoring, benefiting older adults by offering personalized care plans. AI-powered tools help manage chronic conditions and maintain independence, making them a valuable asset in addressing aging challenges. Objectives [...] Read more.
Introduction: Artificial intelligence (AI) is transforming healthcare by enhancing diagnostic accuracy, treatment, and patient monitoring, benefiting older adults by offering personalized care plans. AI-powered tools help manage chronic conditions and maintain independence, making them a valuable asset in addressing aging challenges. Objectives: The objectives are as follows: 1. To identify and describe AI-power-based exercise programs for older adults. 2. To highlight primary evidence gaps in AI interventions for functional improvement and mobility. 3. To evaluate the quality of existing reviews on this topic. Methods: The evidence gap map (EGM) will follow the five-step method, adhering to the Campbell Collaboration guidelines and, if available at the time of reporting, PRISMA-AI standards. Guided by the Metaverse Equitable Rehabilitation Therapy framework, this study will categorize findings across domains like equity, health service integration, interoperability, governance, and humanization. The study will include systematic reviews, randomized controlled trials, and pre-and post-intervention designs. Results will be reported following PRISMA-AI guidelines. We will use AMSTAR-2 Checklist for Analyzing Systematic Reviews on AI Interventions for Improving mobility and function in Older Adults to evaluate the reliability of systematic reviews and focus on internal validity. Conclusions: This comprehensive analysis will act as a critical resource for guiding future research, refining clinical interventions, and influencing policy decisions to enhance AI-driven solutions for aging populations. The EGM aims to bridge existing evidence gaps, fostering a more informed, equitable, and effective approach to AI solutions for older adults. Full article
(This article belongs to the Special Issue Ehealth, Telemedicine, and AI in the Precision Medicine Era)
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