About this Research Topic
In today's data-driven world, the confluence of big data, artificial intelligence (AI), and public health emerges as a pivotal juncture poised to reshape healthcare paradigms. This Research Topic delves deep into this dynamic interplay, aiming to spotlight its transformative potential for global health. As we grapple with multifaceted public health challenges, from pandemics to persistent non-communicable diseases, the amalgamation of big data and AI offers a beacon of hope. These technologies enable the harnessing of vast, heterogeneous datasets, facilitating nuanced analyses that can inform disease prediction, health policy crafting, and targeted interventions.
Through the coupling of Big Data and AI, there have been vast developments in the public health sector. Big Data, characterized by its vast volume, variety, and velocity, provides a rich tapestry of information that, when harnessed effectively, can reveal insights into population health trends, disease patterns, and health determinants. Artificial intelligence (AI) is a useful tool for making sense of this large amount of data because it can learn from and understand large datasets. Collectively, these factors have made it possible for more accurate and planned actions to be taken in public health, shifting the focus from responding to problems to taking advantage of opportunities.
AI algorithms can look for patterns in large amounts of data to help track and control diseases, predict outbreaks, watch the spread of diseases in real-time, and help with public health responses. An example of this is the COVID-19 pandemic, whereby AI models helped understand how the virus behaved and could be treated. These AI models can also identify people and areas that are high-risk, and produce individualized medicines by adapting treatments to their unique genetic patterns. This is critical as policymakers can use Big Data to subsequently review the social factors amongst other factors behind these health disparities. This, of course, is paired with the importance of security and data protection. When handling private health information, strong protections are needed to stop breaches and use without permission.
Through this Research Topic, we aim to explore the challenges, anticipate future trajectories, and catalyze discourse on the multifaceted implications—ethical, operational, and societal—of this potent triad in big data, AI, and public health. Topics of interest include but are not limited to:
• Large datasets in public health
• Clarity of AI processes in the review of public health
• Security and data protection in public health
• Ethical issues and consent to review large datasets in public health
Through the coupling of Big Data and AI, there have been vast developments in the public health sector. Big Data, characterized by its vast volume, variety, and velocity, provides a rich tapestry of information that, when harnessed effectively, can reveal insights into population health trends, disease patterns, and health determinants. Artificial intelligence (AI) is a useful tool for making sense of this large amount of data because it can learn from and understand large datasets. Collectively, these factors have made it possible for more accurate and planned actions to be taken in public health, shifting the focus from responding to problems to taking advantage of opportunities.
AI algorithms can look for patterns in large amounts of data to help track and control diseases, predict outbreaks, watch the spread of diseases in real-time, and help with public health responses. An example of this is the COVID-19 pandemic, whereby AI models helped understand how the virus behaved and could be treated. These AI models can also identify people and areas that are high-risk, and produce individualized medicines by adapting treatments to their unique genetic patterns. This is critical as policymakers can use Big Data to subsequently review the social factors amongst other factors behind these health disparities. This, of course, is paired with the importance of security and data protection. When handling private health information, strong protections are needed to stop breaches and use without permission.
Through this Research Topic, we aim to explore the challenges, anticipate future trajectories, and catalyze discourse on the multifaceted implications—ethical, operational, and societal—of this potent triad in big data, AI, and public health. Topics of interest include but are not limited to:
• Large datasets in public health
• Clarity of AI processes in the review of public health
• Security and data protection in public health
• Ethical issues and consent to review large datasets in public health
Keywords: Artificial Intelligence (AI), Big Data, Data Privacy and Security, Public Health, Personalized Medicine, Public Health Transformation
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