Artificial Intelligence: A New Tool for Public Health

Health Systems Need to Transform Data Collection to Advance Health Equity.

In this inaugural issue of the revived journal, Public Health Reviews, we seek to address a wide audience from the myriad disciplines that comprise the workforce of public health. In this effort, we hope to reach public health educators, researchers, students, policy makers, and practitioners worldwide with evidence-based theoretical and experiential knowledge. Applied nationally and internationally, public health has enormous global potential to save lives and improve quality of life if applied equitably throughout the world. The inaugural issue is therefore devoted to the New Public Health, as the basis for improving population health globally.The central idea behind this issue is to focus on some of the major challenges and possibilities for improving health in the coming decades. The New Public Health involves the interdependence of traditional local, state or provincial, national, and global public health initiatives and their societal and political contexts. European societies, as others, face the challenges of ageing populations, increasing costs and rising public and individual expectations. At the same time, there are many social, regional, and transnational disparities.1,2The New Public Health addresses traditional aspects of public health, as well as the great potential for prevention-oriented healthcare systems. The New Public Health seeks to safeguard and improve individual health in the context of social and economic disparities that exist in varying degrees in all countries and regions. Furthering the New Public Health implies crossfertilization between public health protection, health promotion, disease prevention, health maintenance, and environmental health in the context of improvement of societal, economic, industrial and community habitation policies. Management of health systems and resource allocation are crucial to setting priorities in prevention, and the effective organization and provision of health services are a cornerstone of society's responsibility for the health and well-being of the population.The New Public Health is not a new vision of what health should be; it is a strategic and equitable application of current best practices across the broadest aspects of social policy, public health, and healthcare systems. Ultimately, the New Public Health relies on the adoption of effective policies and programs based on the cumulative experience and available scientific evidence. Such programs and policies must be targeted to individuals, to groups at special risk, to communities, and to societies across the globe.In this issue, we seek to present the interdependence of social and health policy with examples illustrating the interconnectedness of healthcare systems issues. The topics included are not encyclopedic of all the vital issues facing public health systems in the 21st century. Many of those that are not included in this first issue will be addressed in later issues of the journal. Accordingly, we start with the application of science to public health practice, as in the case of Helicobacter pylori, and we move on through health systems, organization of public health, chronic diseases, micronutrient deficiencies and infectious diseases.In this issue, we include:Preface,3 and Foreword: Reminiscences of Helicobacter pylori.4 Respectively by Theodore Tulchinsky, Antoine Flahault, and Robin Warren (Nobel Prize 2005, Western Australia), who relates his experience of scientific discovery that was ultimately embraced by medical and public health practitioners and policy makers. This fascinating historical account provides a vivid example of translating science into practice with major impacts on population health and health services.Editorial: Why a journal called Public Health Reviews?5 Antoine Flahault and Linda Fried present the renewal of this journal with a wider international scope, reaching out to a European and worldwide readership.What is the "New Public Health"? …

RESUMENLa inteligencia artificial (IA) está transformando la Salud Pública al proporcionar herramientas innovadoras para enfrentar desafíos globales complejos. Su capacidad para analizar grandes volúmenes de datos en tiempo real permite mejorar la vigilancia epidemiológica, optimizar la gestión de recursos sanitarios y personalizar intervenciones preventivas. Estas aplicaciones han demostrado ser valiosas en situaciones como pandemias, donde los algoritmos de IA han contribuido a la predicción de brotes, la asignación eficiente de recursos y el diseño de estrategias dirigidas.Sin embargo, la adopción de la IA también plantea importantes desafíos éticos y regulatorios. Cuestiones como la privacidad de los datos, la transparencia de los algoritmos y los sesgos en los modelos destacan la necesidad de marcos normativos robustos que garanticen su uso ético y equitativo. Además, la falta de capacitación de los profesionales de la Salud Pública y la alfabetización digital de las comunidades limitan el impacto potencial de estas tecnologías.Este artículo analiza las aplicaciones prácticas, los desafíos éticos y las estrategias necesarias para una adopción responsable de la IA en la Salud Pública. Se enfatiza la importancia de la capacitación, la colaboración interdisciplinaria y la investigación continua para garantizar que la IA se convierta en una herramienta transformadora que contribuya al bienestar global. Si se implementa de manera ética y sostenible, la IA puede desempeñar un papel crucial en la promoción de la equidad y la calidad en los sistemas de Salud Pública.

Artificial intelligence (AI) algorithms are prone to bias at multiple stages of model development, with potential for exacerbating health disparities. However, bias in imaging AI is a complex topic that encompasses multiple coexisting definitions. Bias may refer to unequal preference to a person or group owing to preexisting attitudes or beliefs, either intentional or unintentional. However, cognitive bias refers to systematic deviation from objective judgment due to reliance on heuristics, and statistical bias refers to differences between true and expected values, commonly manifesting as systematic error in model prediction (ie, a model with output unrepresentative of real-world conditions). Clinical decisions informed by biased models may lead to patient harm due to action on inaccurate AI results or exacerbate health inequities due to differing performance among patient populations. However, while inequitable bias can harm patients in this context, a mindful approach leveraging equitable bias can address underrepresentation of minority groups or rare diseases. Radiologists should also be aware of bias after AI deployment such as automation bias, or a tendency to agree with automated decisions despite contrary evidence. Understanding common sources of imaging AI bias and the consequences of using biased models can guide preventive measures to mitigate its impact. Accordingly, the authors focus on sources of bias at stages along the imaging machine learning life cycle, attempting to simplify potentially intimidating technical terminology for general radiologists using AI tools in practice or collaborating with data scientists and engineers for AI tool development. The authors review definitions of bias in AI, describe common sources of bias, and present recommendations to guide quality control measures to mitigate the impact of bias in imaging AI. Understanding the terms featured in this article will enable a proactive approach to identifying and mitigating bias in imaging AI. Published under a CC BY 4.0 license. Test Your Knowledge questions for this article are available in the supplemental material. See the invited commentary by Rouzrokh and Erickson in this issue.

Background During the past decades, the growth of knowledge to prevent disease on the one hand and the growth of public acceptance to prevent disease on the other hand led to form public health systems. Soon after, health was no longer considered as an individual responsibility only, and more effective interventions against health threats and communicable diseases were developed. As a result, public agencies and authorities were established to implement newly discovered interventions against health threats and to prevent non-communicable diseases. Though, the orientation of public health agencies and authorities shifted from prevention of disease to promotion of health in almost all countries, their public health systems are developed widely differently. While some countries look back to traditionally established public health systems, others are pursuing different approaches to achieve Health in All Policies through policy and legislation in Wales or are now evaluating their public health policies after a ten-years-strategy, like Sweden and still others have only recently (2015) introduced a new Preventive Health Care Act and follow a bottom up approach called “Future Forum Public Health”, like Germany. Objectives This workshop aims at presenting the very different perspectives on the achievement of public health and different systems around Europe. Both, the disparity of the approaches and their different forms, and different stages of their development will be addressed by each presentation. Despite differences of the public health systems in each context, the workshop will focus on identifying shared features and common challenges of the countries with the goal of identifying inspiring examples and discussing criteria of transferability. During the discussion that will follow, different possibilities of cooperation and exchange will be assessed and debated. In particular, the workshop will encompass a presentation from Wales on “the future generation’s act” and will include information on the requirements to turn the legislation into practice. The Swedish input will focus on the Swedish national public health policy and its achievements after ten years. France will give an overview of the structural developments during the last ten years. The presentation from Germany will consist of contrasting a top-down and a bottom-up approach, namely the act to strengthen health promotion and prevention in Germany on the one hand and the future forum public health on the other hand. And last but not least, we will hear about the particular success of the Slovenian example with a newly shaped and largely developed public health system. Key messages Acquiring insight into different approaches to achieve improvements in public health based on alternative systems is important to identify inspiring examples, shared features and common challenges. Identification of criteria of transferability of public health strategies, legislation and policies between countries, and requirements for implementation are crucial for learning from best practice.

A National Research Agenda for Public Health Services and Systems

In an era of diminished resources, public health agencies are being asked to do more with less. Evidence suggests that while revenues at the state and local levels have been flat or increased slightly since 2010, a large number of governmental public health agencies in the United States have been forced to cut programs and/or staff to compensate for the relative paucity of funds at their disposal. At the same time, these agencies have been challenged to effectively deploy diminished resources to develop and implement multi-level interventions in their communities and to address the many determinants of complex chronic diseases, such as diabetes mellitus and heart disease. Infectious diseases like influenza still pose significant threats to public health, placing an additional strain on departmental resources. One way many health departments are addressing the challenge posed by diminished resources is through collaborating with other partners in the public health system to protect community health.

The rapid deployment of Artificial Intelligence (AI) in Anti-Money Laundering (AML) practices within the financial industry presents significant ethical and governance challenges that must be navigated effectively. As financial institutions increasingly adopt AI technologies to enhance their AML efforts, concerns regarding data privacy, algorithmic bias, and transparency emerge. This review explores the ethical implications of AI in AML and offers governance strategies to mitigate risks while ensuring compliance with regulatory frameworks. One of the primary ethical challenges in deploying AI for AML is the potential for algorithmic bias. AI systems trained on historical data may inadvertently perpetuate existing biases, leading to discriminatory practices in transaction monitoring and customer profiling. This raises serious concerns about fairness and equity in the financial sector. Addressing algorithmic bias requires the implementation of rigorous testing and validation processes to ensure AI systems function impartially across diverse populations. Data privacy is another critical issue. The extensive data collection required for effective AML monitoring raises questions about the protection of sensitive customer information. Financial institutions must establish robust data governance frameworks that prioritize privacy and comply with regulations such as the General Data Protection Regulation (GDPR). Ensuring transparency in how data is used and providing clear communication to customers about data practices is essential for building trust. Effective governance frameworks are crucial in navigating these ethical challenges. Financial institutions should adopt a multi-disciplinary approach that includes ethical guidelines, compliance measures, and risk management strategies. Establishing oversight committees can help ensure that AI deployment aligns with ethical standards and regulatory requirements. Furthermore, ongoing training for employees on the ethical use of AI in AML can foster a culture of responsibility and accountability. This review highlights the need for a balanced approach to AI deployment in AML, emphasizing the importance of ethical considerations and governance structures. As the financial industry continues to evolve, addressing these challenges will be essential for maintaining trust, ensuring compliance, and leveraging AI’s potential to enhance AML practices effectively.

Increasing concerns have been raised regarding artificial intelligence (AI) bias, and in response, efforts have been made to pursue AI fairness. In this paper, we argue that the idea of structural injustice serves as a helpful framework for clarifying the ethical concerns surrounding AI bias—including the nature of its moral problem and the responsibility for addressing it—and reconceptualizing the approach to pursuing AI fairness. Using AI in health care as a case study, we argue that AI bias is a form of structural injustice that exists when AI systems interact with other social factors to exacerbate existing social inequalities, making some groups of people more vulnerable to undeserved burdens while conferring unearned benefits to others. The goal of AI fairness, understood this way, is to pursue a more just social structure with the development and use of AI systems when appropriate. We further argue that all participating agents in the unjust social structure associated with AI bias bear a shared responsibility to join collective action with the goal of reforming the social structure, and we provide a list of practical recommendations for agents in various social positions to contribute to this collective action.

This study explores innovative, ethical leadership approaches using artificial intelligence (AI) and fractal geometry in public health while fostering sustainable business practices within public health systems. The research employs a qualitative methodology based on case studies, secondary data analysis, and fractal-based AI algorithm evaluations. It examines advanced algorithms' technical applications in public health settings, improving data privacy, copyright, and intellectual property protection. The study finds that fractal algorithms offer robust solutions for promoting ethical leadership in AI-driven public health systems. Fractal geometry's complexity and self-similarity improve predictive modeling, resource allocation, and system transparency while ensuring legal and ethical compliance. By applying fractal algorithms, public health organizations can improve privacy protection, intellectual property management, and ethical governance. The study highlights the need for further research on practical applications, optimization of fractal algorithms, and overcoming the computational demands associated with their deployment in public health. Ethical leadership approaches supported by fractal algorithms can drive more equitable and secure public health interventions, enhancing trust in AI-driven solutions and reducing healthcare access and outcomes disparities. This research presents a novel integration of fractal geometry and AI to address critical ethical issues in public health, providing innovative solutions for data privacy, intellectual property protection, and ethical leadership practices.

For public health to meet the challenges of COVID-19 and beyond, to increase evidence-based decision making, and to realize public health as a public good, we need a broad, intentional movement to build coalitions among public health experts in academia, research, practice, and the public sector. The authors discuss how failure to invest in public health systems, inadequate health communications, and poor public health practice during the COVID-19 pandemic opened the door for informal systems of information sharing and the consequences thereof. The lesson that public health professionals and public health associations need to draw from the COVID-19 pandemic is that communicating effectively is critical to ensuring that evidence, even emerging evidence, guides decision-makers. We discuss the challenges of evidence-based decision-making in public health policymaking and highlight examples of academic institutions that have invested in public health knowledge translation. Finally, we describe how the Doctor of Public Health (DrPH) degree was designed to bridge communities of practice, and what is needed for those with DrPH training to stand up and meet the moment.

he rapid progress in implementing Artificial Intelligence (AI) across various domains such as healthcare decision-making, medical diagnosis, and others has raised significant concerns regarding the fairness and bias embedded within AI systems. This is particularly crucial in sectors like healthcare, employment, criminal justice, credit scoring, and the emerging field of generative AI models (GenAI) producing synthetic media. Such systems can lead to unfair outcomes and perpetuate existing inequalities, including biases ingrained in the synthetic data representation of individuals.This survey paper provides a concise yet comprehensive examination of fairness and bias in AI, encompassing their origins, ramifications, and potential mitigation strategies. We scrutinize sources of bias, including data, algorithmic, and human decision biases, shedding light on the emergent issue of generative AI bias where models may replicate and amplify societal stereotypes. Assessing the societal impact of biased AI systems, we spotlight the perpetuation of inequalities and the reinforcement of harmful stereotypes, especially as generative AI gains traction in shaping public perception through generated content.Various proposed mitigation strategies are explored, with an emphasis on the ethical considerations surrounding their implementation. We stress the necessity of interdisciplinary collaboration to ensure the effectiveness of these strategies. Through a systematic literature review spanning multiple academic disciplines, we define AI bias and its various types, delving into the nuances of generative AI bias. We discuss the adverse effects of AI bias on individuals and society, providing an overview of current approaches to mitigate bias, including data preprocessing, model selection, and post-processing. Unique challenges posed by generative AI models are highlighted, underscoring the importance of tailored strategies to address them effectively.Addressing bias in AI necessitates a holistic approach, involving diverse and representative datasets, enhanced transparency, and accountability in AI systems, and exploration of alternative AI paradigms prioritizing fairness and ethical considerations. This survey contributes to the ongoing discourse on developing fair and unbiased AI systems by outlining the sources, impacts, and mitigation strategies related to AI bias, with a particular focus on the burgeoning field of generative AI.

Advancing AI in healthcare: A comprehensive review of best practices

By providing affordable healthcare to many Americans for the first time, the Affordable Care Act increases demand for public mental health services. It is, however, unclear if states’ provision standards for supply of mental health services will be able to accommodate this demand increase. Both the demand and supply of public mental health services vary within states; it is necessary to measure both locally. In this paper, we estimate the prevalence of mental illness within 30 geographical regions in the state of Virginia, a representative example of how many states organize their public mental health systems and how mental illness prevalence can be measured locally. Our methodology extends the analysis in Stern (Health Serv. Outcomes Res. Methods 14:109–155, 2014) by covering an entire state and accounting for peoples’ insurance status. The latter allows us to compare estimates of demand for public mental health services among those 30 geographical regions. We find that over 66,000 uninsured and Medicaid-insured individuals in Virginia are not provided with public mental health services. The deficit varies locally, with several regions having no deficit and others having 5000 or more untreated people. We also estimate that a large portion of the unserved people with mental illness are uninsured but would be insured for mental health services through Medicaid if Virginia were to accept the Medicaid expansion associated with the Affordable Care Act. These results provide evidence that there is significant variation in the demand for and public health systems’ ability to supply mental health services within states. This implies states can better serve populations relying on mental health care by allocating scarce public mental health dollars to localities reflecting their need.

Background Artificial Intelligence (AI) holds the potential to fundamentally transform public health communication - presenting promising opportunities alongside profound challenges and risks. This two-wave Delphi study therefore invited practitioners and scholars to assess the potential impact of AI on the risk communication, community engagement, and infodemic management (RCCE-IM) of public health authorities in European and Central Asian countries. Methods In two successive online surveys, 54 experts - both RCCE-IM practitioners and scholars with expertise in (digital) health communication, risk communication, and AI - from 28 countries provided their assessment of the following analytical dimensions: (1) opportunities, challenges, and risks of AI, (2) conditions for the responsible use of AI, (3) potential future scenarios, and (4) prioritized actions. The first Delphi wave followed an open and exploratory approach, while the second wave sought to prioritize and rank key findings from the previous round. The survey instruments included open-ended and closed-ended questions that were analyzed through statistical analysis, inductive and deductive coding. Results Results showed that AI holds various opportunities for the RCCE-IM of public health communicators. Challenges and risks, such as algorithmic bias or concerns about inclusivity, equally affect RCCE-IM efforts. Experts identified seven principles for the responsible use of AI (e.g., fair, human-centered) and predicted optimistic (e.g., optimized workflows) and pessimistic (e.g., flood of (mis)information) scenarios for the future impact of AI. Prioritized actions range from regulation, resource allocation, and feedback to capacity building, public trust, and education. Conclusions To responsibly navigate the opportunities, challenges, and risks of AI for public health emergency communication, clear guiding principles, ongoing critical evaluation, and multi-sectoral collaboration are needed. Key messages • AI is likely to transform public health emergency communication. • A set of agreed-upon principles will support public health authorities in the responsible management of the opportunities, challenges, and risks of AI.