About this Research Topic
This collection is a Volume III following on from the Topic Editors’ previously successful Research Topics, Extracting Insights from Digital Public Health Data using Artificial Intelligence and Extracting Insights from Digital Public Health Data using Artificial Intelligence, Volume II.
The integration of artificial intelligence (AI) with digital public health data presents a transformative opportunity in understanding and addressing population health challenges. This Research Topic seeks to explore the innovative methods and applications at the intersection of AI and public health informatics, and showcase cutting-edge research and best practices in harnessing AI for extracting insights from digital public health data, fostering advancements in healthcare delivery and population health management.
With the proliferation of digital health data sources such as electronic health records, wearable devices, and social media platforms, AI-driven analytics offer unprecedented potential to uncover valuable insights. From predictive modeling of disease outbreaks to personalized health interventions, AI techniques including machine learning and natural language processing enable the extraction of actionable knowledge. However, alongside these opportunities come ethical considerations and challenges in data integration and algorithm development.
This Research Topic aims to achieve several key goals:
1) Provide a platform for researchers, practitioners, and policymakers to disseminate their latest findings, methodologies and applications in leveraging artificial intelligence for analyzing digital public health data;
2) Foster interdisciplinary collaboration between experts in AI, public health informatics, epidemiology, and data science, facilitating the exchange of ideas and approaches to addressing complex public health challenges and highlighting innovative AI algorithms and techniques that enhance the understanding of population health trends, disease surveillance and healthcare interventions;
3) Identify ethical considerations, privacy concerns and implementation challenges associated with the use of AI in public health settings;
4) Advance knowledge and practices in utilizing AI to improve public health outcomes and healthcare delivery.
Key areas of interest for this Research Topic include but are not limited to:
• Novel AI algorithms for analyzing digital public health data;
• Predictive modeling of disease incidence and prevalence;
• Sentiment analysis of social media data for public health surveillance;
• Integration of heterogeneous health data sources for comprehensive analysis;
• Geospatial analysis of environmental and population health data;
• Real-time monitoring and early detection of disease outbreaks;
• Automated surveillance systems for infectious diseases;
• Personalized health interventions based on digital biomarkers;
• Ethical and privacy considerations in the use of AI for public health
• Implementation challenges and best practices for deploying AI solutions in public health settings.
The integration of artificial intelligence (AI) with digital public health data presents a transformative opportunity in understanding and addressing population health challenges. This Research Topic seeks to explore the innovative methods and applications at the intersection of AI and public health informatics, and showcase cutting-edge research and best practices in harnessing AI for extracting insights from digital public health data, fostering advancements in healthcare delivery and population health management.
With the proliferation of digital health data sources such as electronic health records, wearable devices, and social media platforms, AI-driven analytics offer unprecedented potential to uncover valuable insights. From predictive modeling of disease outbreaks to personalized health interventions, AI techniques including machine learning and natural language processing enable the extraction of actionable knowledge. However, alongside these opportunities come ethical considerations and challenges in data integration and algorithm development.
This Research Topic aims to achieve several key goals:
1) Provide a platform for researchers, practitioners, and policymakers to disseminate their latest findings, methodologies and applications in leveraging artificial intelligence for analyzing digital public health data;
2) Foster interdisciplinary collaboration between experts in AI, public health informatics, epidemiology, and data science, facilitating the exchange of ideas and approaches to addressing complex public health challenges and highlighting innovative AI algorithms and techniques that enhance the understanding of population health trends, disease surveillance and healthcare interventions;
3) Identify ethical considerations, privacy concerns and implementation challenges associated with the use of AI in public health settings;
4) Advance knowledge and practices in utilizing AI to improve public health outcomes and healthcare delivery.
Key areas of interest for this Research Topic include but are not limited to:
• Novel AI algorithms for analyzing digital public health data;
• Predictive modeling of disease incidence and prevalence;
• Sentiment analysis of social media data for public health surveillance;
• Integration of heterogeneous health data sources for comprehensive analysis;
• Geospatial analysis of environmental and population health data;
• Real-time monitoring and early detection of disease outbreaks;
• Automated surveillance systems for infectious diseases;
• Personalized health interventions based on digital biomarkers;
• Ethical and privacy considerations in the use of AI for public health
• Implementation challenges and best practices for deploying AI solutions in public health settings.
Keywords: Digital Public Health, Artificial Intelligence, Data Analytics, Public Health Informatics, Epidemiology, Predictive Modeling, Health Data Mining, Healthcare Analytics, Disease Surveillance, Health Behavior Analysis, Ethical considerations
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
