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

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

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 (2 papers)

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13 pages, 1649 KiB  

Open AccessArticle

Impact of the COVID-19 Pandemic on Life Expectancy in South Korea, 2019–2022

by Soojin Song and Daroh Lim

Healthcare 2025, 13(3), 258; https://doi.org/10.3390/healthcare13030258 - 28 Jan 2025

Viewed by 597

Abstract

Objective: This study investigated changes in life expectancy due to the COVID-19 pandemic by analyzing the contributions of age, sex, and cause of death in 2019 and 2022. Methods: Korea’s simplified life table and cause-of-death statistics from 2019 to 2022 were used to [...] Read more.

Objective: This study investigated changes in life expectancy due to the COVID-19 pandemic by analyzing the contributions of age, sex, and cause of death in 2019 and 2022. Methods: Korea’s simplified life table and cause-of-death statistics from 2019 to 2022 were used to assess mortality changes by age, sex, and cause of death during the pandemic. Joinpoint regression analysis was applied to detect trends, and the Arriaga decomposition method was used to quantify the contributions of age, sex, and cause of death to life expectancy changes. Results: Joinpoint regression identified a slow increase in life expectancy in 2007 and a decline in 2020, coinciding with the COVID-19 pandemic. Life expectancy decreased markedly for men (−0.36 years per year, 95%CI: −0.68 to −0.03) and women (−0.45 years per year, 95%CI: −0.71 to −0.18). Age-specific contributions revealed declines across age groups, with the steepest reductions in the older population (80 years or older: −0.35 years for men; −0.52 years for women). Women (−0.68 years) contributed more to the decline in life expectancy than men (−0.41 years). COVID-19 ranked as the third leading cause of death in 2022, significantly contributing to the decline in life expectancy among the older population (aged 80 years or older: −0.306 years for men, −0.408 years for women). Women in Korea were more affected than men, reducing the sex-specific gap in life expectancy by 0.3 years. Conclusions: The COVID-19 pandemic significantly impacted the life expectancy in Korea, particularly among older adults, with women experiencing a greater decline than men. These findings emphasize the need for targeted public health strategies to address age and sex disparities in future pandemics. Before the pandemic, non-communicable diseases such as malignant neoplasms, heart disease, and cerebrovascular disease dominated Korea’s top 10 causes of death. During the pandemic, however, COVID-19 rose to third place by 2022. Notably, intentional self-harm (suicide) contributed to an increase in life expectancy, suggesting shifts in the relative impact of various causes of death. Full article

(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities, 2nd Edition)

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24 pages, 1499 KiB  

Open AccessArticle

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

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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

(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities, 2nd Edition)

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