Background: Population aging is one of the most significant global demographic changes of the 21st century, driven by increased life expectancy and declining fertility rates. This phenomenon presents both achievements and challenges for public health systems worldwide. Aims: On the one hand, advances in healthcare and socio-economic conditions have contributed to longer lives and improved quality of life for older adults. On the other hand, aging populations are increasingly affected by chronic diseases, greriatric syndromes, and multimorbidity, leading to greater healthcare demands and higher associated costs. Methods: This manuscript explores evidence on regards of the impact of aging on healthcare and economic systems, emphasizing the need for a paradigm shift toward healthy aging. Results: Healthy aging, as defined by the World Health Organization, focuses on the maintenance of intrinsic capacity, physical, mental, and social well-being throughout life. It highlights the importance of preventive healthcare, proper nutrition, and regular physical activity in delaying the onset of chronic conditions and maintaining functional independence. Furthermore, the manuscript addresses the challenges faced by healthcare infrastructures and pension systems as they adapt to aging populations, with particular attention to the strain caused by workforce shortages and the rising need for long-term care. Discussion: A coordinated public health approach is essential to promote healthy aging and mitigate the economic and societal impacts of population aging. Conclusions: This paper underscores the need for integrated health policies and multidisciplinary care models to ensure that longer life expectancy is accompanied by better quality of life for older individuals.

Antimicrobial resistance (AMR) poses a critical global health threat, necessitating innovative approaches in antimicrobial stewardship (AMS). Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools in this domain, enabling data-driven interventions to optimize antibiotic use and combat resistance. This comprehensive review explores the multifaceted role of AI and ML models in enhancing antimicrobial stewardship efforts across healthcare systems. AI-powered predictive analytics can identify patterns of resistance, forecast outbreaks, and guide personalized antibiotic therapies by leveraging large-scale clinical and epidemiological data. ML algorithms facilitate rapid pathogen identification, resistance profiling, and real-time monitoring, enabling precise decision making. These technologies also support the development of advanced diagnostic tools, reducing the reliance on broad-spectrum antibiotics and fostering timely, targeted treatments. In public health, AI-driven surveillance systems improve the detection of AMR trends and enhance global monitoring capabilities. By integrating diverse data sources—such as electronic health records, laboratory results, and environmental data—ML models provide actionable insights to policymakers, healthcare providers, and public health officials. Additionally, AI applications in antimicrobial stewardship programs (ASPs) promote adherence to prescribing guidelines, evaluate intervention outcomes, and optimize resource allocation. Despite these advancements, challenges such as data quality, algorithm transparency, and ethical considerations must be addressed to maximize the potential of AI and ML in this field. Future research should focus on developing interpretable models and fostering interdisciplinary collaborations to ensure the equitable and sustainable integration of AI into antimicrobial stewardship initiatives.

A mass vaccination center is a location, normally used for nonhealthcare activities, set up for high-volume and high-speed vaccinations during infectious disease emergencies. The high contagiousness and mortality of COVID-19 and the complete lack of population immunity posed an extraordinary threat for global health. The aim of our research was to collect and review previous experiences on mass vaccination centers. On 4 April 2021, we developed a rapid review searching four electronic databases: PubMed/Medline, Scopus, EMBASE, Google Scholar and medRxiv. From a total of 2312 papers, 15 of them were included in the current review. Among them, only one article described a COVID-19 vaccination center; all of the others referred to other vaccinations, in particular influenza. The majority were conducted in the United States, and were simulations or single-day experiences to practice a mass vaccination after bioterrorist attacks. Indeed, all of them were published after September 11 attacks. Regarding staff, timing and performance, the data were highly heterogenous. Several studies used as a model the Center for Disease Control and Prevention guidelines. Results highlighted the differences around the definition, layout and management of a mass vaccination center, but some aspects can be considered as a core aspect. In light of this, we suggested a potential definition. The current review answers to the urgency of organizing a mass vaccination center during the COVID-19 pandemic, highlighting the most important organizational aspects that should be considered in the planning.

The COVID-19 pandemic has underscored the critical importance of adaptable and resilient public health systems capable of rapid response to emerging health crises. This paper synthesizes the lessons learned from the COVID-19 vaccination campaign and explores strategies to enhance vaccine uptake in the post-pandemic era. Key challenges identified include logistical, economic, sociocultural, and policy dimensions that impact vaccination efforts, particularly in low-resource settings. The analysis highlights the need for resilient supply chains, effective communication, community engagement, and equitable access to healthcare resources. The rapid development and deployment of mRNA vaccines exemplify the potential of innovative vaccine technologies, though public trust and acceptance remain crucial. Strategies such as partnerships with local leaders, tailored messaging, and integration of digital tools are essential for combating vaccine hesitancy. By applying these insights, future vaccination campaigns can be more efficient, equitable, and resilient, ultimately improving public health outcomes globally. This paper aims to inform policy and practice, ensuring that public health strategies are evidence based and context specific, thus better preparing for future health challenges.

The growing challenge of antimicrobial resistance (AMR) has underscored the urgent need for robust antimicrobial stewardship programs (AMS). Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools to support enhanced decision-making in AMS. This systematic review and meta-analysis aims to evaluate the impact of AI in AMS and compare its effectiveness with traditional risk systems. PubMed/MEDLINE, Scopus, EMBASE, and Web of Science were searched to identify studies published up to July 2024. Any studies that evaluated the use of AI/ML in AMS compared with conventional decision-making approaches were eligible. Outcomes of interested were predictive performance metrics and diagnostic accuracy. The meta-estimate was performed pooling standardized mean difference, and effect size (ES) measured as Cohen's d with a 95% confidence interval (CI). The risk of bias was assessed using the QUADAS-AI tool. Out of 3,458 studies, 27 were included, demonstrating that ML models outperform traditional methods in terms of sensitivity [1.93 (0.48-3.39) p = 0.009], and negative predictive value [1.66 (0.86-2.46), p < 0.001] but not in terms of area under the curve, accuracy, specificity, positive predictive value, when random effect models were applied. Our results revealed that ML tools offer promising enhancements to traditional AMS strategies. However, high heterogeneity, inconsistent results between fixed and random effect models, and limited use of external validation in retrieved studies raise concerns about the generalizability of the findings. Furthermore, the lack of representation from outpatient and pediatric settings highlights a critical equity gap in the application of these technologies.

Background: Older adults are highly vulnerable to infectious diseases due to immunosenescence, multimorbidity, and atypical presentations. Artificial intelligence (AI) offers promising opportunities to improve diagnosis, prognosis, treatment, and continuity of care in this population. This review summarizes current applications of AI in the management of infections in older adults across diagnostic, prognostic, therapeutic, and preventive domains. Methods: We conducted a narrative review of peer-reviewed studies retrieved from PubMed, Scopus, and Web of Science, focusing on AI-based tools for infection diagnosis, risk prediction, antimicrobial stewardship, prevention of healthcare-associated infections, and post-discharge care in individuals aged ≥65 years. Results: AI models, including machine learning, deep learning, and natural language processing techniques, have demonstrated high performance in detecting infections such as sepsis, pneumonia, and healthcare-associated infections (Area Under the Curve AUC up to 0.98). Prognostic algorithms integrating frailty and functional status enhance the prediction of mortality, complications, and readmission. AI-driven clinical decision support systems contribute to optimized antimicrobial therapy and timely interventions, while remote monitoring and telemedicine applications support safer hospital-to-home transitions and reduced 30-day readmissions. However, the implementation of these technologies is limited by the underrepresentation of frail older adults in training datasets, lack of real-world validation in geriatric settings, and the insufficient explainability of many models. Additional barriers include system interoperability issues and variable digital infrastructure, particularly in long-term care and community settings. Conclusions: AI has strong potential to support predictive and personalized infection management in older adults. Future research should focus on developing geriatric-specific, interpretable models, improving system integration, and fostering interdisciplinary collaboration to ensure safe and equitable implementation.