Topic Editors

Centre Tisp, Istituto Superiore Di Sanita, 000161 Rome, Italy
Department of Electronic Engineering, University of Rome Tor Vergata, 00133 Rome, Italy

Artificial Intelligence in Public Health: Current Trends and Future Possibilities, 2nd Edition

Abstract submission deadline
31 October 2025
Manuscript submission deadline
31 December 2025
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1173

Topic Information

Dear Colleagues,

Due to the COVID-19 pandemic, we are witnessing a growing scientific interest in the development and application of artificial intelligence in the health domain. Research in this area is strategic for the development of health systems and is inextricably linked to the development of digital health, both as regards the collection, -monitoring and management of information, and as regards the management of hospital and connected government information systems. Think, for example, of the opportunities presented by wearable monitoring, big data, and robotic surgery. The applications of artificial intelligence have received growing interest in many sectors, such as: organ, functional tissue and cell diagnostics; care robotics, assisting in interventions, rehabilitation and supporting the communication and assistance of disabled people; the biomedicine sector, from genetics to modeling; and precision and personalized biomedicine.

A statement by Henry Ford reported that "real progress happens only when the advantages of a new technology become available to everybody".

The consolidation of technologies based on artificial intelligence in the health domain is intended to bring benefits to everyone, from the stakeholder to the patient, in the form of equity of care.

Artificial intelligence in the future will have a strong impact on:

  • The prevention of the onset of diseases in the individual and in society
  • The provision of personal care and assistance.
  • Society trends regarding diseases and the impact of biological and behavioral factors.
  • Organization of hospital activities with regard to treatment, diagnostic and decision-making processes.

Thanks to artificial intelligence, on the one hand, big data will help us to predict diseases on an individual and collective basis and to identify and correct population behaviors; on the other hand, wearable technologies will allow us to monitor and collect individual medical information and to calibrate the care process. The integration of artificial intelligence with virtual reality and augmented reality will allow us to create both virtual medicine services that citizens can access in a simple and direct way, and robotic surgery applications that are increasingly effective and safe.

This topic is very broad, and ranges from scientific development to applications in the health domain, and it also includes ethical and training issues.

This Topic invites authors to contribute on aspects of the research on, development, and application of artificial intelligence in current applications in the health domain and in future scenarios of use.

In this Topic, original research articles, reviews, commentaries, opinions, viewpoints, communications and brief reports are welcome. Research areas may include (but are not limited to) the following:

  • Artificial neural networks
  • Deep learning
  • Care robotics
  • Natural language processing
  • Social intelligence
  • Virtual reality
  • Augmented reality
  • Medical decision making
  • Disease monitoring, prediction, diagnosis, and classification
  • Patient monitoring
  • Hospital organization
  • Diagnostic imaging
  • Digital pathology
  • Digital radiology.

We look forward to receiving your contributions.

Prof. Dr. Daniele Giansanti
Dr. Giovanni Costantini
Topic Editors

Keywords

  • artificial intelligence
  • neural networks
  • big data
  • robotics
  • healthcare
  • virtual reality
  • augmented reality
  • digital health
  • digital radiology
  • digital pathology

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
AI
ai
3.1 7.2 2020 18.9 Days CHF 1600 Submit
Applied Sciences
applsci
2.5 5.3 2011 18.4 Days CHF 2400 Submit
Bioengineering
bioengineering
3.8 4.0 2014 16.4 Days CHF 2700 Submit
Healthcare
healthcare
2.4 3.5 2013 20.3 Days CHF 2700 Submit
International Journal of Environmental Research and Public Health
ijerph
- 7.3 2004 25.8 Days CHF 2500 Submit
Journal of Clinical Medicine
jcm
3.0 5.7 2012 16 Days CHF 2600 Submit

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Published Papers (1 paper)

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24 pages, 1499 KiB  
Article
Explainable Artificial Intelligence for Predicting Attention Deficit Hyperactivity Disorder in Children and Adults
by Zineb Namasse, Mohamed Tabaa, Zineb Hidila and Samar Mouchawrab
Healthcare 2025, 13(2), 155; https://doi.org/10.3390/healthcare13020155 - 15 Jan 2025
Viewed by 615
Abstract
Attention Deficit Hyperactivity Disorder (ADHD) is a disorder that starts in childhood, sometimes persisting into adulthood. It puts a strain on their social, professional, family, and environmental lives, which can exacerbate disorders such as anxiety, depression, and bipolar disorder. Background/Objectives: This paper [...] Read more.
Attention Deficit Hyperactivity Disorder (ADHD) is a disorder that starts in childhood, sometimes persisting into adulthood. It puts a strain on their social, professional, family, and environmental lives, which can exacerbate disorders such as anxiety, depression, and bipolar disorder. Background/Objectives: This paper aims to predict ADHD in children and adults and explain the main factors impacting this disorder. Methods: We start by introducing the main symptoms and challenges ADHD poses for children and adults such as epilepsy and depression. Then, we present the results of existing research on three ADHD comorbidities: anxiety, depression, and bipolar disorder, and their possible continuity in adulthood with therapeutic implications. After that, we explain the impact of this disorder and its relationship with these comorbidities on the affected patient’s health and environment and list proposed treatments. We propose a methodology for predicting this impairment in children and adults by using Machine Learning algorithms (ML), Explainable Artificial Intelligence (XAI), and two datasets, the National Survey for Children’s Health (NSCH) (2022) for the children and the ADHD|Mental Health for the adults. Results: Logistic Regression (LR) was the most suitable algorithm for children, with an accuracy of 99%. As for adults, the XGBoost (XGB) was the most performant ML method, with an accuracy of 100%. Conclusions: Lack of sleep and excessive smiling/laughing are among the factors having an impact on ADHD for children, whereas anxiety and depression affect ADHD adults. Full article
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