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	16.9 Days	CHF 2400	Submit
Bioengineering bioengineering	4.6	4.2	2014	17.7 Days	CHF 2700	Submit
Healthcare healthcare	2.8	2.7	2013	19.5 Days	CHF 2700	Submit
International Journal of Environmental Research and Public Health ijerph	-	5.4	2004	29.6 Days	CHF 2500	Submit
Journal of Clinical Medicine jcm	3.9	5.4	2012	17.9 Days	CHF 2600	Submit
AI ai	-	-	2020	20.8 Days	CHF 1600	Submit

MDPI Topics is cooperating with Preprints.org and has built a direct connection between MDPI journals and Preprints.org. Authors are encouraged to enjoy the benefits by posting a preprint at Preprints.org prior to publication:

1. Immediately share your ideas ahead of publication and establish your research priority;
2. Protect your idea from being stolen with this time-stamped preprint article;
3. Enhance the exposure and impact of your research;
4. Receive feedback from your peers in advance;
5. Have it indexed in Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (12 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

All Applied Sciences Bioengineering Healthcare IJERPH JCM AI

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

18 pages, 3662 KiB  

Open AccessArticle

Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach

by Zubair Saeed, Othmane Bouhali, Jim Xiuquan Ji, Rabih Hammoud, Noora Al-Hammadi, Souha Aouadi and Tarraf Torfeh

Bioengineering 2024, 11(5), 410; https://doi.org/10.3390/bioengineering11050410 - 23 Apr 2024

Viewed by 125

Abstract

Brain cancer is a life-threatening disease requiring close attention. Early and accurate diagnosis using non-invasive medical imaging is critical for successful treatment and patient survival. However, manual diagnosis by radiologist experts is time-consuming and has limitations in processing large datasets efficiently. Therefore, efficient [...] Read more.

Brain cancer is a life-threatening disease requiring close attention. Early and accurate diagnosis using non-invasive medical imaging is critical for successful treatment and patient survival. However, manual diagnosis by radiologist experts is time-consuming and has limitations in processing large datasets efficiently. Therefore, efficient systems capable of analyzing vast amounts of medical data for early tumor detection are urgently needed. Deep learning (DL) with deep convolutional neural networks (DCNNs) emerges as a promising tool for understanding diseases like brain cancer through medical imaging modalities, especially MRI, which provides detailed soft tissue contrast for visualizing tumors and organs. DL techniques have become more and more popular in current research on brain tumor detection. Unlike traditional machine learning methods requiring manual feature extraction, DL models are adept at handling complex data like MRIs and excel in classification tasks, making them well-suited for medical image analysis applications. This study presents a novel Dual DCNN model that can accurately classify cancerous and non-cancerous MRI samples. Our Dual DCNN model uses two well-performed DL models, i.e., inceptionV3 and denseNet121. Features are extracted from these models by appending a global max pooling layer. The extracted features are then utilized to train the model with the addition of five fully connected layers and finally accurately classify MRI samples as cancerous or non-cancerous. The fully connected layers are retrained to learn the extracted features for better accuracy. The technique achieves 99%, 99%, 98%, and 99% of accuracy, precision, recall, and f1-scores, respectively. Furthermore, this study compares the Dual DCNN’s performance against various well-known DL models, including DenseNet121, InceptionV3, ResNet architectures, EfficientNetB2, SqueezeNet, VGG16, AlexNet, and LeNet-5, with different learning rates. This study indicates that our proposed approach outperforms these established models in terms of performance. Full article

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

►▼ Show Figures

Graphical abstract

13 pages, 1491 KiB  

Open AccessArticle

A Novel Method for Determining Fibrin/Fibrinogen Degradation Products and Fibrinogen Threshold Criteria via Artificial Intelligence in Massive Hemorrhage during Delivery with Hematuria

by Yasunari Miyagi, Katsuhiko Tada, Ichiro Yasuhi, Keisuke Tsumura, Yuka Maegawa, Norifumi Tanaka, Tomoya Mizunoe, Ikuko Emoto, Kazuhisa Maeda, Kosuke Kawakami and on behalf of the Collaborative Research in National Hospital Organization Network Pediatric and Perinatal Group

J. Clin. Med. 2024, 13(6), 1826; https://doi.org/10.3390/jcm13061826 - 21 Mar 2024

Viewed by 650

Abstract

(1) Background: Although the diagnostic criteria for massive hemorrhage with organ dysfunction, such as disseminated intravascular coagulation associated with delivery, have been empirically established based on clinical findings, strict logic has yet to be used to establish numerical criteria. (2) Methods: A dataset [...] Read more.

(1) Background: Although the diagnostic criteria for massive hemorrhage with organ dysfunction, such as disseminated intravascular coagulation associated with delivery, have been empirically established based on clinical findings, strict logic has yet to be used to establish numerical criteria. (2) Methods: A dataset of 107 deliveries with >2000 mL of blood loss, among 13,368 deliveries, was obtained from nine national perinatal centers in Japan between 2020 and 2023. Twenty-three patients had fibrinogen levels <170 mg/dL, which is the initiation of coagulation system failure, according to our previous reports. Three of these patients had hematuria. We used six machine learning methods to identify the borderline criteria dividing the fibrinogen/fibrin/fibrinogen degradation product (FDP) planes, using 15 coagulation fibrinolytic factors. (3) Results: The boundaries of hematuria development on a two-dimensional plane of fibrinogen and FDP were obtained. A positive FDP–fibrinogen/3–60 (mg/dL) value indicates hematuria; otherwise, the case is nonhematuria, as demonstrated by the support vector machine method that seemed the most appropriate. (4) Conclusions: Using artificial intelligence, the borderline criterion was obtained, which divides the fibrinogen/FDP plane for patients with hematuria that could be considered organ dysfunction in massive hemorrhage during delivery; this method appears to be useful. Full article

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

►▼ Show Figures

Figure 1

25 pages, 4781 KiB  

Open AccessArticle

Brain Tumor Detection and Categorization with Segmentation of Improved Unsupervised Clustering Approach and Machine Learning Classifier

by Usharani Bhimavarapu, Nalini Chintalapudi and Gopi Battineni

Bioengineering 2024, 11(3), 266; https://doi.org/10.3390/bioengineering11030266 - 08 Mar 2024

Viewed by 1063

Abstract

There is no doubt that brain tumors are one of the leading causes of death in the world. A biopsy is considered the most important procedure in cancer diagnosis, but it comes with drawbacks, including low sensitivity, risks during biopsy treatment, and a [...] Read more.

There is no doubt that brain tumors are one of the leading causes of death in the world. A biopsy is considered the most important procedure in cancer diagnosis, but it comes with drawbacks, including low sensitivity, risks during biopsy treatment, and a lengthy wait for results. Early identification provides patients with a better prognosis and reduces treatment costs. The conventional methods of identifying brain tumors are based on medical professional skills, so there is a possibility of human error. The labor-intensive nature of traditional approaches makes healthcare resources expensive. A variety of imaging methods are available to detect brain tumors, including magnetic resonance imaging (MRI) and computed tomography (CT). Medical imaging research is being advanced by computer-aided diagnostic processes that enable visualization. Using clustering, automatic tumor segmentation leads to accurate tumor detection that reduces risk and helps with effective treatment. This study proposed a better Fuzzy C-Means segmentation algorithm for MRI images. To reduce complexity, the most relevant shape, texture, and color features are selected. The improved Extreme Learning machine classifies the tumors with 98.56% accuracy, 99.14% precision, and 99.25% recall. The proposed classifier consistently demonstrates higher accuracy across all tumor classes compared to existing models. Specifically, the proposed model exhibits accuracy improvements ranging from 1.21% to 6.23% when compared to other models. This consistent enhancement in accuracy emphasizes the robust performance of the proposed classifier, suggesting its potential for more accurate and reliable brain tumor classification. The improved algorithm achieved accuracy, precision, and recall rates of 98.47%, 98.59%, and 98.74% on the Fig share dataset and 99.42%, 99.75%, and 99.28% on the Kaggle dataset, respectively, which surpasses competing algorithms, particularly in detecting glioma grades. The proposed algorithm shows an improvement in accuracy, of approximately 5.39%, in the Fig share dataset and of 6.22% in the Kaggle dataset when compared to existing models. Despite challenges, including artifacts and computational complexity, the study’s commitment to refining the technique and addressing limitations positions the improved FCM model as a noteworthy advancement in the realm of precise and efficient brain tumor identification. Full article

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

►▼ Show Figures

Figure 1

11 pages, 3656 KiB  

Open AccessPerspective

A Neural Modelling Tool for Non-Linear Influence Analyses and Perspectives of Applications in Medical Research

by Antonello Pasini and Stefano Amendola

Appl. Sci. 2024, 14(5), 2148; https://doi.org/10.3390/app14052148 - 04 Mar 2024

Viewed by 538

Abstract

Neural network models are often used to analyse non-linear systems; here, in cases of small datasets, we review our complementary approach to deep learning with the purpose of highlighting the importance and roles (linear, non-linear or threshold) of certain variables (assumed as causal) [...] Read more.

Neural network models are often used to analyse non-linear systems; here, in cases of small datasets, we review our complementary approach to deep learning with the purpose of highlighting the importance and roles (linear, non-linear or threshold) of certain variables (assumed as causal) in determining the behaviour of a target variable; this also allows us to make predictions for future scenarios of these causal variables. We present a neural tool endowed with an ensemble strategy and its applications to influence analyses in terms of pruning, attribution and future predictions (free code issued). We describe some case studies on climatic applications which show reliable results and the potentialities of our method for medical studies. The discovery of the importance and role (linear, non-linear or threshold) of causal variables and the possibility of applying the relationships found to future scenarios could lead to very interesting applications in medical research and the study and treatment of cancer, which are proposed in this paper. Full article

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

►▼ Show Figures

Figure 1

21 pages, 1112 KiB  

Open AccessReview

Reimagining Radiology: A Comprehensive Overview of Reviews at the Intersection of Mobile and Domiciliary Radiology over the Last Five Years

by Graziano Lepri, Francesco Oddi, Rosario Alfio Gulino and Daniele Giansanti

Bioengineering 2024, 11(3), 216; https://doi.org/10.3390/bioengineering11030216 - 24 Feb 2024

Viewed by 1115

Abstract

(Background) Domiciliary radiology, which originated in pioneering studies in 1958, has transformed healthcare, particularly during the COVID-19 pandemic, through advancements such as miniaturization and digitization. This evolution, driven by the synergy of advanced technologies and robust data networks, reshapes the intersection of domiciliary [...] Read more.

(Background) Domiciliary radiology, which originated in pioneering studies in 1958, has transformed healthcare, particularly during the COVID-19 pandemic, through advancements such as miniaturization and digitization. This evolution, driven by the synergy of advanced technologies and robust data networks, reshapes the intersection of domiciliary radiology and mobile technology in healthcare delivery. (Objective) The objective of this study is to overview the reviews in this field with reference to the last five years to face the state of development and integration of this practice in the health domain. (Methods) A review was conducted on PubMed and Scopus, applying a standard checklist and a qualification process. The outcome detected 21 studies. (Key Content and Findings) The exploration of mobile and domiciliary radiology unveils a compelling and optimistic perspective. Notable strides in this dynamic field include the integration of Artificial Intelligence (AI), revolutionary applications in telemedicine, and the educational potential of mobile devices. Post-COVID-19, telemedicine advances and the influential role of AI in pediatric radiology signify significant progress. Mobile mammography units emerge as a solution for underserved women, highlighting the crucial importance of early breast cancer detection. The investigation into domiciliary radiology, especially with mobile X-ray equipment, points toward a promising frontier, prompting in-depth research for comprehensive insights into its potential benefits for diverse populations. The study also identifies limitations and suggests future exploration in various domains of mobile and domiciliary radiology. A key recommendation stresses the strategic prioritization of multi-domain technology assessment initiatives, with scientific societies’ endorsement, emphasizing regulatory considerations for responsible and ethical technology integration in healthcare practices. The broader landscape of technology assessment should aim to be innovative, ethical, and aligned with societal needs and regulatory standards. (Conclusions) The dynamic state of the field is evident, with active exploration of new frontiers. This overview also provides a roadmap, urging scholars, industry players, and regulators to collectively contribute to the further integration of this technology in the health domain. Full article

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

►▼ Show Figures

Figure 1

16 pages, 1768 KiB  

Open AccessReview

The Effects of Artificial Intelligence Chatbots on Women’s Health: A Systematic Review and Meta-Analysis

by Hyun-Kyoung Kim

Healthcare 2024, 12(5), 534; https://doi.org/10.3390/healthcare12050534 - 23 Feb 2024

Viewed by 1354

Abstract

Purpose: This systematic review and meta-analysis aimed to investigate the effects of artificial intelligence chatbot interventions on health outcomes in women. Methods: Ten relevant studies published between 2019 and 2023 were extracted from the PubMed, Cochrane Library, EMBASE, CINAHL, and RISS databases in [...] Read more.

Purpose: This systematic review and meta-analysis aimed to investigate the effects of artificial intelligence chatbot interventions on health outcomes in women. Methods: Ten relevant studies published between 2019 and 2023 were extracted from the PubMed, Cochrane Library, EMBASE, CINAHL, and RISS databases in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. This review focused on experimental studies concerning chatbot interventions in women’s health. The literature was assessed using the ROB 2 quality appraisal checklist, and the results were visualized with a risk-of-bias visualization program. Results: This review encompassed seven randomized controlled trials and three single-group experimental studies. Chatbots were effective in addressing anxiety, depression, distress, healthy relationships, cancer self-care behavior, preconception intentions, risk perception in eating disorders, and gender attitudes. Chatbot users experienced benefits in terms of internalization, acceptability, feasibility, and interaction. A meta-analysis of three studies revealed significant effects in reducing anxiety (I² = 0%, Q = 8.10, p < 0.017), with an effect size of −0.30 (95% CI, −0.42 to −0.18). Conclusions: Artificial intelligence chatbot interventions had positive effects on physical, physiological, and cognitive health outcomes. Using chatbots may represent pivotal nursing interventions for female populations to improve health status and support women socially as a form of digital therapy. Full article

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

►▼ Show Figures

Figure 1

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 12 | Viewed by 3014

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)

►▼ Show Figures

Figure 1

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 6 | Viewed by 1863

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 2464

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)

►▼ Show Figures

Figure 1

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 40 | Viewed by 8544

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 1531

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)

►▼ Show Figures

Figure 1

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 2668

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)

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-12

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.