Using artificial intelligence to improve public health: a narrative review

Leveraging artificial intelligence in vaccine development: A narrative review

Vaccine development stands as a cornerstone of public health efforts, pivotal in curbing infectious diseases and reducing global morbidity and mortality. However, traditional vaccine development methods are often time-consuming, costly, and inefficient. The advent of artificial intelligence (AI) has ushered in a new era in vaccine design, offering unprecedented opportunities to expedite the process. This narrative review explores the role of AI in vaccine development, focusing on antigen selection, epitope prediction, adjuvant identification, and optimization strategies. AI algorithms, including machine learning and deep learning, leverage genomic data, protein structures, and immune system interactions to predict antigenic epitopes, assess immunogenicity, and prioritize antigens for experimentation. Furthermore, AI-driven approaches facilitate the rational design of immunogens and the identification of novel adjuvant candidates with optimal safety and efficacy profiles. Challenges such as data heterogeneity, model interpretability, and regulatory considerations must be addressed to realize the full potential of AI in vaccine development. Integrating emerging technologies, such as single-cell omics and synthetic biology, promises to enhance vaccine design precision and scalability. This review underscores the transformative impact of AI on vaccine development and highlights the need for interdisciplinary collaborations and regulatory harmonization to accelerate the delivery of safe and effective vaccines against infectious diseases.

Artificial intelligence empowering public health education: prospects and challenges

Artificial Intelligence (AI) is revolutionizing public health education through its capacity for intricate analysis of large-scale health datasets and the tailored dissemination of health-related information and interventions. This article conducts a profound exploration into the integration of AI within public health, accentuating its scientific foundations, prospective progress, and practical application scenarios. It underscores the transformative potential of AI in crafting individualized educational programs, developing sophisticated behavioral models, and informing the creation of health policies. The manuscript strives to thoroughly evaluate the extant landscape of AI applications in public health, scrutinizing critical challenges such as the propensity for data bias and the imperative of safeguarding privacy. By dissecting these issues, the article contributes to the conversation on how AI can be harnessed responsibly and effectively, ensuring that its application in public health education is both ethically grounded and equitable. The paper's significance is multifold: it aims to provide a blueprint for policy formulation, offer actionable insights for public health authorities, and catalyze the progression of health interventions toward increasingly sophisticated and precise approaches. Ultimately, this research anticipates fostering an environment where AI not only augments public health education but also does so with a steadfast commitment to the principles of justice and inclusivity, thereby elevating the standard and reach of health education initiatives globally.

Association of retinal image-based, deep learning cardiac BioAge with telomere length and cardiovascular biomarkers

SIGNIFICANCE

Our retinal image-based deep learning (DL) cardiac biological age (BioAge) model could facilitate fast, accurate, noninvasive screening for cardiovascular disease (CVD) in novel community settings and thus improve outcome with those with limited access to health care services.

PURPOSE

This study aimed to determine whether the results issued by our DL cardiac BioAge model are consistent with the known trends of CVD risk and the biomarker leukocyte telomere length (LTL), in a cohort of individuals from the UK Biobank.

METHODS

A cross-sectional cohort study was conducted using those individuals in the UK Biobank who had LTL data. These individuals were divided by sex, ranked by LTL, and then grouped into deciles. The retinal images were then presented to the DL model, and individual's cardiac BioAge was determined. Individuals within each LTL decile were then ranked by cardiac BioAge, and the mean of the CVD risk biomarkers in the top and bottom quartiles was compared. The relationship between an individual's cardiac BioAge, the CVD biomarkers, and LTL was determined using traditional correlation statistics.

RESULTS

The DL cardiac BioAge model was able to accurately stratify individuals by the traditional CVD risk biomarkers, and for both males and females, those issued with a cardiac BioAge in the top quartile of their chronological peer group had a significantly higher mean systolic blood pressure, hemoglobin A1c, and 10-year Pooled Cohort Equation CVD risk scores compared with those individuals in the bottom quartile (p<0.001). Cardiac BioAge was associated with LTL shortening for both males and females (males: -0.22, r2 = 0.04; females: -0.18, r2 = 0.03).

CONCLUSIONS

In this cross-sectional cohort study, increasing CVD risk whether assessed by traditional biomarkers, CVD risk scoring, or our DL cardiac BioAge, CVD risk model, was inversely related to LTL. At a population level, our data support the growing body of evidence that suggests LTL shortening is a surrogate marker for increasing CVD risk and that this risk can be captured by our novel DL cardiac BioAge model.

Trending ticks: using Google Trends data to understand tickborne disease prevention

Introduction

Ticks and pathogens they carry seriously impact human and animal health, with some diseases like Lyme and Alpha-gal syndrome posing risks. Searching for health information online can change people's health and preventive behaviors, allowing them to face the tick risks. This study aimed to predict the potential risks of tickborne diseases by examining individuals' online search behavior.

Methods

By scrutinizing the search trends across various geographical areas and timeframes within the United States, we determined outdoor activities associated with potential risks of tick-related diseases. Google Trends was used as the data collection and analysis tool due to its accessibility to big data on people's online searching behaviors. We interact with vast amounts of population search data and provide inferences between population behavior and health-related phenomena. Data were collected in the United States from April 2022 to March 2023, with some terms about outdoor activities and tick risks.

Results and Discussion

Results highlighted the public's risk susceptibility and severity when participating in activities. Our results found that searches for terms related to tick risk were associated with the five-year average Lyme Disease incidence rates by state, reflecting the predictability of online health searching for tickborne disease risks. Geographically, the results revealed that the states with the highest relative search volumes for tick-related terms were predominantly located in the Eastern region. Periodically, terms can be found to have higher search records during summer. In addition, the results showed that terms related to outdoor activities, such as "corn maze," "hunting," "u-pick," and "park," have moderate associations with tick-related terms. This study provided recommendations for effective communication strategies to encourage the public's adoption of health-promoting behaviors. Displaying warnings in the online search results of individuals who are at high risk for tick exposure or collaborating with outdoor activity locations to disseminate physical preventive messages may help mitigate the risks associated with tickborne diseases.

Assessment of Saudi Public Perceptions and Opinions towards Artificial Intelligence in Health Care

Background and Objectives: The healthcare system in Saudi Arabia is growing rapidly with the utilization of advanced technologies. Therefore, this study aimed to assess the Saudi public perceptions and opinions towards artificial intelligence (AI) in health care. Materials and Methods: This cross-sectional web-based questionnaire study was conducted between January and April 2024. Data were analyzed from 830 participants. The perceptions of the public towards AI were assessed using 21-item questionnaires. Results: Among the respondents, 69.4% were males and 46% of them were aged above 41 years old. A total of 84.1% of the participants knew about AI, while 61.1% of them believed that AI is a tool that helps healthcare professionals, and 12.5% of them thought that AI may replace the physician, pharmacist, or nurse in the healthcare system. With regard to opinion on the widespread use of AI, 45.8% of the study population believed that healthcare professionals will be improved with the widespread use of artificial intelligence. The mean perception score of AI among males was 38.4 (SD = 6.1) and this was found to be higher than for females at 37.7 (SD = 5.3); however, no significant difference was observed (p = 0.072). Similarly, the mean perception score was higher among young adults aged between 20 and 25 years at 38.9 (SD = 6.1) compared to other age groups, but indicating no significant association between them (p = 0.198). Conclusions: The results showed that the Saudi public had a favorable opinion and perceptions of AI in health care. This suggests that health management recommendations should be made regarding how to successfully integrate and use medical AI while maintaining patient safety.

Transforming Public Health Practice With Generative Artificial Intelligence

Public health practice appears poised to undergo a transformative shift as a result of the latest advancements in artificial intelligence (AI). These changes will usher in a new era of public health, charged with responding to deficiencies identified during the COVID-19 pandemic and managing investments required to meet the health needs of the twenty-first century. In this Commentary, we explore how AI is being used in public health, and we describe the advanced capabilities of generative AI models capable of producing synthetic content such as images, videos, audio, text, and other digital content. Viewing the use of AI from the perspective of health departments in the United States, we examine how this new technology can support core public health functions with a focus on near-term opportunities to improve communication, optimize organizational performance, and generate novel insights to drive decision making. Finally, we review the challenges and risks associated with these technologies, offering suggestions for health officials to harness the new tools to accomplish public health goals.

Eco-friendly revenues for healthcare: assessing the relationship between green taxation, public health expenditures, and life expectancy in China

Introduction

The synergy of green taxation, public health expenditures, and life expectancy emerges as a compelling narrative in the intricate symphony of environmental responsibility and public well-being. Therefore, this study examine the impact of green taxation on life expectancy and the moderating role of public health expenditure on the said nexus, particularly in the context of China, an emerging economy.

Methods

Statistical data is collected from the National Bureau of Statistics of China to empirically examine the proposed relationships. The dataset contains provincial data across years.

Results

Using fixed-effect and system GMM regression models alongwith control variables, the results found a positive and statistically significant influence of green taxation on life expectancy. Moreover, public health expenditures have a positive and statistically significant partial moderating impact on the direct relationship.

Discussion

These findings suggest that the higher cost of pollution encourages individuals and businesses to shift to less environmentally harmful alternatives, subsequently improving public health. Moreover, government investment in the health sector increases the availability and accessibility of health facilities; thus, the positive impact of green taxation on public health gets more pronounced. The findings significantly contribute to the fields of environmental and health economics and provide a new avenue of research for the academic community and policymakers.

Artificial intelligence for the optimal management of community-acquired pneumonia

PURPOSE OF REVIEW

This timely review explores the integration of artificial intelligence (AI) into community-acquired pneumonia (CAP) management, emphasizing its relevance in predicting the risk of hospitalization. With CAP remaining a global public health concern, the review highlights the need for efficient and reliable AI tools to optimize resource allocation and improve patient outcomes.

RECENT FINDINGS

Challenges in CAP management delve into the application of AI in predicting CAP-related hospitalization risks, and complications, and mortality. The integration of AI-based risk scores in managing CAP has the potential to enhance the accuracy of predicting patients at higher risk, facilitating timely intervention and resource allocation. Moreover, AI algorithms reduce variability associated with subjective clinical judgment, promoting consistency in decision-making, and provide real-time risk assessments, aiding in the dynamic management of patients with CAP.

SUMMARY

The development and implementation of AI-tools for hospitalization in CAP represent a transformative approach to improving patient outcomes. The integration of AI into healthcare has the potential to revolutionize the way we identify and manage individuals at risk of severe outcomes, ultimately leading to more efficient resource utilization and better overall patient care.

Translating the COVID-19 epidemiological situation into policies and measures: the Belgian experience

The COVID-19 pandemic led to sustained surveillance efforts, which made unprecedented volumes and types of data available. In Belgium, these data were used to conduct a targeted and regular assessment of the epidemiological situation. In addition, management tools were developed, incorporating key indicators and thresholds, to define risk levels and offer guidance to policy makers. Categorizing risk into various levels provided a stable framework to monitor the COVID-19 epidemiological situation and allowed for clear communication to authorities. Although translating risk levels into specific public health measures has remained challenging, this experience was foundational for future evaluation of the situation for respiratory infections in general, which, in Belgium, is now based on a management tool combining different data sources.