Artificial Intelligence in Public Health: Current Trends and Future Possibilities

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
Applied Sciences applsci	2.7	4.5	2011	15.8 Days	CHF 2300	Submit
Bioengineering bioengineering	4.6	4.2	2014	15.6 Days	CHF 2700	Submit
Healthcare healthcare	2.8	2.7	2013	21.7 Days	CHF 2700	Submit
International Journal of Environmental Research and Public Health ijerph	-	5.4	2004	22 Days	CHF 2500	Submit
Journal of Clinical Medicine jcm	3.9	5.4	2012	19.7 Days	CHF 2600	Submit

Published Papers (6 papers)

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All Applied Sciences Bioengineering Healthcare IJERPH JCM

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14 pages, 1837 KiB  

Open AccessReview

The Utility of Language Models in Cardiology: A Narrative Review of the Benefits and Concerns of ChatGPT-4

by

Dhir Gala

and

Amgad N. Makaryus

Int. J. Environ. Res. Public Health 2023, 20(15), 6438; https://doi.org/10.3390/ijerph20156438 - 25 Jul 2023

Cited by 3 | Viewed by 1668

Abstract

Artificial intelligence (AI) and language models such as ChatGPT-4 (Generative Pretrained Transformer) have made tremendous advances recently and are rapidly transforming the landscape of medicine. Cardiology is among many of the specialties that utilize AI with the intention of improving patient care. Generative [...] Read more.

Artificial intelligence (AI) and language models such as ChatGPT-4 (Generative Pretrained Transformer) have made tremendous advances recently and are rapidly transforming the landscape of medicine. Cardiology is among many of the specialties that utilize AI with the intention of improving patient care. Generative AI, with the use of its advanced machine learning algorithms, has the potential to diagnose heart disease and recommend management options suitable for the patient. This may lead to improved patient outcomes not only by recommending the best treatment plan but also by increasing physician efficiency. Language models could assist physicians with administrative tasks, allowing them to spend more time on patient care. However, there are several concerns with the use of AI and language models in the field of medicine. These technologies may not be the most up-to-date with the latest research and could provide outdated information, which may lead to an adverse event. Secondly, AI tools can be expensive, leading to increased healthcare costs and reduced accessibility to the general population. There is also concern about the loss of the human touch and empathy as AI becomes more mainstream. Healthcare professionals would need to be adequately trained to utilize these tools. While AI and language models have many beneficial traits, all healthcare providers need to be involved and aware of generative AI so as to assure its optimal use and mitigate any potential risks and challenges associated with its implementation. In this review, we discuss the various uses of language models in the field of cardiology. Full article

(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities)
(This article belongs to the Section Digital Health)

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18 pages, 396 KiB  

Open AccessReview

The Artificial Intelligence in Teledermatology: A Narrative Review on Opportunities, Perspectives, and Bottlenecks

by

Daniele Giansanti

Int. J. Environ. Res. Public Health 2023, 20(10), 5810; https://doi.org/10.3390/ijerph20105810 - 12 May 2023

Cited by 4 | Viewed by 1303

Abstract

Artificial intelligence (AI) is recently seeing significant advances in teledermatology (TD), also thanks to the developments that have taken place during the COVID-19 pandemic. In the last two years, there was an important development of studies that focused on opportunities, perspectives, and problems [...] Read more.

Artificial intelligence (AI) is recently seeing significant advances in teledermatology (TD), also thanks to the developments that have taken place during the COVID-19 pandemic. In the last two years, there was an important development of studies that focused on opportunities, perspectives, and problems in this field. The topic is very important because the telemedicine and AI applied to dermatology have the opportunity to improve both the quality of healthcare for citizens and the workflow of healthcare professionals. This study conducted an overview on the opportunities, the perspectives, and the problems related to the integration of TD with AI. The methodology of this review, following a standardized checklist, was based on: (I) a search of PubMed and Scopus and (II) an eligibility assessment, using parameters with five levels of score. The outcome highlighted that applications of this integration have been identified in various skin pathologies and in quality control, both in eHealth and mHealth. Many of these applications are based on Apps used by citizens in mHealth for self-care with new opportunities but also open questions. A generalized enthusiasm has been registered regarding the opportunities and general perspectives on improving the quality of care, optimizing the healthcare processes, minimizing costs, reducing the stress in the healthcare facilities, and in making citizens, now at the center, more satisfied. However, critical issues have emerged related to: (a) the need to improve the process of diffusion of the Apps in the hands of citizens, with better design, validation, standardization, and cybersecurity; (b) the need for better attention paid to medico-legal and ethical issues; and (c) the need for the stabilization of international and national regulations. Targeted agreement initiatives, such as position statements, guidelines, and/or consensus initiatives, are needed to ensure a better result for all, along with the design of both specific plans and shared workflows. Full article

(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities)
(This article belongs to the Section Digital Health)

18 pages, 1137 KiB  

Open AccessReview

Harnessing Machine Learning in Tackling Domestic Violence—An Integrative Review

by

Vivian Hui

,

Rose E. Constantino

and

Young Ji Lee

Int. J. Environ. Res. Public Health 2023, 20(6), 4984; https://doi.org/10.3390/ijerph20064984 - 12 Mar 2023

Cited by 1 | Viewed by 1803

Abstract

Domestic violence (DV) is a public health crisis that threatens both the mental and physical health of people. With the unprecedented surge in data available on the internet and electronic health record systems, leveraging machine learning (ML) to detect obscure changes and predict [...] Read more.

Domestic violence (DV) is a public health crisis that threatens both the mental and physical health of people. With the unprecedented surge in data available on the internet and electronic health record systems, leveraging machine learning (ML) to detect obscure changes and predict the likelihood of DV from digital text data is a promising area health science research. However, there is a paucity of research discussing and reviewing ML applications in DV research. Methods: We extracted 3588 articles from four databases. Twenty-two articles met the inclusion criteria. Results: Twelve articles used the supervised ML method, seven articles used the unsupervised ML method, and three articles applied both. Most studies were published in Australia (n = 6) and the United States (n = 4). Data sources included social media, professional notes, national databases, surveys, and newspapers. Random forest (n = 9), support vector machine (n = 8), and naïve Bayes (n = 7) were the top three algorithms, while the most used automatic algorithm for unsupervised ML in DV research was latent Dirichlet allocation (LDA) for topic modeling (n = 2). Eight types of outcomes were identified, while three purposes of ML and challenges were delineated and are discussed. Conclusions: Leveraging the ML method to tackle DV holds unprecedented potential, especially in classification, prediction, and exploration tasks, and particularly when using social media data. However, adoption challenges, data source issues, and lengthy data preparation times are the main bottlenecks in this context. To overcome those challenges, early ML algorithms have been developed and evaluated on DV clinical data. Full article

(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities)
(This article belongs to the Section Digital Health)

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12 pages, 332 KiB  

Open AccessArticle

Artificial Intelligence and Public Health: An Exploratory Study

by

David Jungwirth

and

Daniela Haluza

Int. J. Environ. Res. Public Health 2023, 20(5), 4541; https://doi.org/10.3390/ijerph20054541 - 03 Mar 2023

Cited by 20 | Viewed by 5781

Abstract

Artificial intelligence (AI) has the potential to revolutionize research by automating data analysis, generating new insights, and supporting the discovery of new knowledge. The top 10 contribution areas of AI towards public health were gathered in this exploratory study. We utilized the “text-davinci-003” [...] Read more.

Artificial intelligence (AI) has the potential to revolutionize research by automating data analysis, generating new insights, and supporting the discovery of new knowledge. The top 10 contribution areas of AI towards public health were gathered in this exploratory study. We utilized the “text-davinci-003” model of GPT-3, using OpenAI playground default parameters. The model was trained with the largest training dataset any AI had, limited to a cut-off date in 2021. This study aimed to test the ability of GPT-3 to advance public health and to explore the feasibility of using AI as a scientific co-author. We asked the AI asked for structured input, including scientific quotations, and reviewed responses for plausibility. We found that GPT-3 was able to assemble, summarize, and generate plausible text blocks relevant for public health concerns, elucidating valuable areas of application for itself. However, most quotations were purely invented by GPT-3 and thus invalid. Our research showed that AI can contribute to public health research as a team member. According to authorship guidelines, the AI was ultimately not listed as a co-author, as it would be done with a human researcher. We conclude that good scientific practice also needs to be followed for AI contributions, and a broad scientific discourse on AI contributions is needed. Full article

(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities)
(This article belongs to the Section Digital Health)

12 pages, 2526 KiB  

Open AccessArticle

Research on the Application of Artificial Intelligence in Public Health Management: Leveraging Artificial Intelligence to Improve COVID-19 CT Image Diagnosis

by

Tiancheng He

,

Hong Liu

,

Zhihao Zhang

,

Chao Li

and

Youmei Zhou

Int. J. Environ. Res. Public Health 2023, 20(2), 1158; https://doi.org/10.3390/ijerph20021158 - 09 Jan 2023

Cited by 2 | Viewed by 1162

Abstract

Since the start of 2020, the outbreak of the Coronavirus disease (COVID-19) has been a global public health emergency, and it has caused unprecedented economic and social disaster. In order to improve the diagnosis efficiency of COVID-19 patients, a number of researchers have [...] Read more.

Since the start of 2020, the outbreak of the Coronavirus disease (COVID-19) has been a global public health emergency, and it has caused unprecedented economic and social disaster. In order to improve the diagnosis efficiency of COVID-19 patients, a number of researchers have conducted extensive studies on applying artificial intelligence techniques to the analysis of COVID-19-related medical images. The automatic segmentation of lesions from computed tomography (CT) images using deep learning provides an important basis for the quantification and diagnosis of COVID-19 cases. For a deep learning-based CT diagnostic method, a few of accurate pixel-level labels are essential for the training process of a model. However, the translucent ground-glass area of the lesion usually leads to mislabeling while performing the manual labeling operation, which weakens the accuracy of the model. In this work, we propose a method for correcting rough labels; that is, to hierarchize these rough labels into precise ones by performing an analysis on the pixel distribution of the infected and normal areas in the lung. The proposed method corrects the incorrectly labeled pixels and enables the deep learning model to learn the infected degree of each infected pixel, with which an aiding system (named DLShelper) for COVID-19 CT image diagnosis using the hierarchical labels is also proposed. The DLShelper targets lesion segmentation from CT images, as well as the severity grading. The DLShelper assists medical staff in efficient diagnosis by providing rich auxiliary diagnostic information (including the severity grade, the proportions of the lesion and the visualization of the lesion area). A comprehensive experiment based on a public COVID-19 CT image dataset is also conducted, and the experimental results show that the DLShelper significantly improves the accuracy of segmentation for the lesion areas and also achieves a promising accuracy for the severity grading task. Full article

(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities)
(This article belongs to the Section Digital Health)

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4 pages, 271 KiB  

Open AccessEditorial

Artificial Intelligence in Public Health: Current Trends and Future Possibilities

by

Daniele Giansanti

Int. J. Environ. Res. Public Health 2022, 19(19), 11907; https://doi.org/10.3390/ijerph191911907 - 21 Sep 2022

Cited by 10 | Viewed by 2063

Abstract

Artificial intelligence (AI) is a discipline that studies whether and how intelligent computer systems that can simulate the capacity and behaviour of human thought can be created [...] Full article

(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities)
(This article belongs to the Section Digital Health)

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