Precision in Prevention and Health Surveillance: How Artificial Intelligence May Improve the Time of Identification of Health Concerns through Social Media Content Analysis.

Objective: To explore how artificial intelligence (AI) methodologies, particularly through the analysis of social media content, can enhance "precision in prevention and health surveillance" (2024 Yearbook topic). The focus is on leveraging advanced data analytics to improve the timeliness and accuracy of identifying emerging health concerns, thus enabling more proactive and effective health interventions.

Methods: A comprehensive literature search strategy was conducted on PubMed, focusing on papers published in 2023 related to consumer health informatics, precision prevention, and the intersection with social media. The search aimed to identify studies that utilized AI and machine learning techniques to analyse social media data for health surveillance purposes. Bibliometric analyses were applied to the retrieved articles, and tools such as "Bibliometrix" were used to assess keyword frequencies, co-occurrence networks, and thematic maps. The studies were then independently reviewed and screened for relevance, with a final selection of 10 articles made based on their alignment with the 2024 Yearbook topic and their methodological innovation.

Results: The analysis of 89 articles revealed several key themes and findings. Social media data offers a rich source of real-time insights into public health trends, and encompasses diverse demographic groups. AI methodologies, including machine learning, natural language processing (NLP), and deep learning, play a crucial role in extracting and analysing health-related information from social media content. The integration of AI in health surveillance can provide early warnings of potential health crises, as demonstrated by studies on topics such as suicide prevention, mental health, and the impact of social media use on e-cigarette consumption among youth. Ethical and privacy considerations are paramount, necessitating robust data anonymization and transparent data handling practices. Advanced AI techniques, such as transformer-based topic modelling and federated learning, enhance the precision and security of health surveillance systems. The document highlights several case studies that demonstrate the practical applications of AI in health surveillance, such as monitoring public discussions about delta-8 THC and assessing suicide-related tweets and their association with help-seeking behaviour in the US.

Conclusion: Integrating AI and social media content analysis in precision prevention and health surveillance has significant potential to improve public health outcomes. By leveraging real-time, comprehensive data from social media platforms, AI can enhance the timeliness and accuracy of identifying health concerns. Addressing ethical and privacy challenges is crucial to ensure responsible and effective implementation. The continuous advancement of AI technologies will play a critical role in safeguarding public health and responding to emerging health threats.