Background/Objectives: The COVID-19 pandemic highlighted how infodemics—an excessive amount of both accurate and misleading information—undermine health responses. Artificial intelligence (AI) and digital tools have been increasingly applied to monitor, detect, and counter health misinformation online. This scoping review aims to systematically map digital and AI-based interventions, describing their applications, outcomes, ethical and equity implications, and policy frameworks. Methods: This review followed the Joanna Briggs Institute methodology and was reported according to PRISMA-ScR. The protocol was preregistered on the Open Science Framework . Searches were conducted in PubMed/MEDLINE, Scopus, Web of Science, and CINAHL (January 2017–March 2025). Two reviewers independently screened titles/abstracts and full texts; disagreements were resolved by a third reviewer. Data extraction included study characteristics, populations, technologies, outcomes, thematic areas, and domains. Quantitative synthesis used descriptive statistics with 95% confidence intervals. Results: A total of 63 studies were included, most published between 2020 and 2024. The majority originated from the Americas (41.3%), followed by Europe (15.9%), the Western Pacific (9.5%), and other regions; 22.2% had a global scope. The most frequent thematic areas were monitoring/surveillance (54.0%) and health communication (42.9%), followed by education/training, AI/ML model development, and digital engagement tools. The domains most often addressed were applications (63.5%), responsiveness, policies/strategies, ethical concerns, and equity/accessibility. Conclusions: AI and digital tools provide significant contributions in detecting misinformation, strengthening surveillance, and promoting health literacy. However, evidence remains heterogeneous, with geographic imbalances, reliance on proxy outcomes, and limited focus on vulnerable groups. Scaling these interventions requires transparent governance, multilingual datasets, ethical safeguards, and integration into public health infrastructures.

Post-COVID-19, influenza A(H3N2) has re-emerged with accelerated genetic diversification. The rise in antigenically drifted subclades raises concerns regarding immune escape and vaccine mismatch. To synthesize the available evidence on the post-pandemic evolution of influenza A(H3N2), with particular attention to emerging subclades and their potential public health implications, a scoping review (PRISMA-ScR) was conducted across six databases in January 2026, including studies on genomic surveillance, antigenic characterization, and vaccine effectiveness (VE). Twenty studies were included. While clade 2a.3a.1 (J lineage) predominated, reports highlighted the rapid emergence of the antigenically distinct K subclade (formerly J.2.4.1). Genetic and laboratory assays (HI/neutralization) confirmed immune escape and reduced vaccine match. Observational estimates indicated diminished VE against drifted strains, though no consistent increase in clinical severity was observed. Post-pandemic A(H3N2) is defined by rapid drift and diversification. Emerging variants like the K subclade challenge vaccine selection and seasonal preparedness. Integrated surveillance remains vital for timely vaccine updates and mitigating public health impact.