Artificial Intelligence in Public Health: Advancing Multidisciplinary Applications for Population Health

About this Research Topic

Background

Artificial Intelligence (AI) is revolutionizing the public health landscape by enabling sophisticated data analytics, enhancing diagnostic capabilities, and improving healthcare delivery systems. With the exponential growth of healthcare data - ranging from electronic health records and genomic data to wearable device outputs - AI offers unprecedented opportunities to address critical public health challenges. Machine Learning (ML), Deep Learning (DL), Large Language Models (LLMs), and natural language processing (NLP) are being increasingly integrated into public health applications, from epidemiological analysis to clinical decision-making. These technological advancements provide transformative potential in areas such as disease prediction, early detection, automated medical coding, and personalized healthcare planning. However, challenges remain in ensuring data quality, algorithmic bias, ethical considerations, and interdisciplinary collaboration, making this an evolving and dynamic field of research.

This Research Topic aims to explore the application of AI methodologies to address critical challenges in public health across multiple domains. We invite contributions that leverage ML techniques for improving health data analytics in epidemiology, biostatistics, health economics, and healthcare service planning. In the realm of Digital Medicine, we seek to explore how AI enhances existing digital health platforms and improves upon standard care practices, with particular attention to AI-enabled diagnostic and therapeutic tools that show promise for clinical implementation. We encourage submissions on the integration of ML and DL techniques for medical image recognition, supporting clinical diagnostics such as burn assessment, cancer grading, and robotic-assisted rehabilitation monitoring.

Additionally, we aim to address key challenges such as resource development, large-scale screening, and multimodal integration by:
- Developing AI-based Resources: Fostering the creation of specialized datasets for medical language processing, multimodal analysis, and federated learning approaches that ensure data privacy and compliance with regulations.
- Enhancing Large-Scale Screening: Investigating AI-driven approaches for identifying linguistic and non-linguistic biomarkers to detect cognitive decline, depression, and other health conditions at an early stage.
- Advancing Multimodal Systems: Promoting research on integrating multiple data sources (text, imaging, physiological signals) to improve predictive accuracy and clinical decision support systems.

More precisely, this Research Topic welcomes interdisciplinary research contributions that address the intersection of AI and public health, covering diverse perspectives such as data science, epidemiology, biostatistics, bioengineering, bioinformatics, IT, and robotics. Specific themes of interest include:

- Predictive Analysis and Epidemic Modeling: ML algorithms for predicting disease spread and enabling timely interventions.
- Processing and Analyzing Big Data: AI-driven methods for managing and analyzing complex healthcare datasets.
- Optimizing Diagnosis and Prognosis: Deep learning models for medical imaging and early disease detection.
- AI applications in early disease detection and public health policy optimization.
- LLM-powered classification of clinical reports and speech-to-text technologies.
- Federated learning and decentralized AI techniques for secure data sharing.
- Real-Time Monitoring and Health Surveillance: AI applications for continuous health monitoring.

Authors should consider addressing challenges and limitations of AI applications in public health contexts, including issues of data quality, algorithmic bias, privacy concerns, regulatory considerations, and implementation barriers. Submissions will be considered for publication across multiple Frontiers journals based on their disciplinary focus. We invite researchers, clinicians, and technologists to contribute cutting-edge studies that showcase the potential of AI in transforming public health practice and policy.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Community Case Study
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• General Commentary
• Hypothesis and Theory
• Methods
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Community Case Study
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Artificial Intelligence in Healthcare, Machine Learning and Deep Learning, Predictive Modeling and Big Data Analytics, Multimodal Data Analysis, . Digital Health Technologies

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.

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