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.

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.

Background: Mosquito-borne viral diseases are a growing global health threat, and artificial intelligence (AI) and machine learning (ML) are increasingly proposed as forecasting tools to support early-warning and response. However, the available evidence is fragmented across pathogens, settings and modelling approaches. This review provides, to the best of our knowledge, the first comprehensive comparative assessment of AI/ML models forecasting mosquito-borne viral diseases in human populations, jointly synthesising predictive performance across model families and appraising both methodological quality and operational readiness. Methods: Following PRISMA 2020, we searched PubMed, Embase and Scopus up to August 2025. We included studies applying AI/ML or statistical models to predict arboviral incidence, outbreaks or temporal trends and reporting at least one quantitative performance metric. Given the substantial heterogeneity in outcomes, predictors and time–space scales, we conducted a descriptive synthesis. Risk of bias and applicability were evaluated using PROBAST. Results: Ninety-eight studies met the inclusion criteria, of which 91 focused on dengue. The forecasts spanned national to city-level settings and annual-to-weekly resolutions. Across classification tasks, tree-ensemble models showed the most consistent performance, with accuracies typically above 0.85, while classical ML and deep-learning models showed wider variability. For regression tasks, errors increased with temporal horizon and spatial aggregation: short-term, fine-scale forecasts (e.g., weekly city level) often achieved low absolute errors, whereas long-horizon national models frequently exhibited very large errors and unstable performance. PROBAST assessment indicated that most studies (63/98) were at high risk of bias, with only 24 judged at low risk and limited external validation. Conclusions: AI/ML models, especially tree-ensemble approaches, show strong potential for short-term, fine-scale forecasting, but their reliability drops substantially at broader spatial and temporal scales. Most remain research-stage, with limited external validation and minimal operational deployment. This review clarifies current capabilities and highlights three priorities for real-world use: standardised reporting, rigorous external validation, and context-specific calibration.

Background/Objectives: Adult vaccination remains suboptimal, particularly among older adults and individuals with chronic conditions. Hospitals represent a strategic setting for improving vaccination coverage among these high-risk populations. This systematic review and meta-analysis evaluated hospital-based interventions aimed at enhancing vaccine uptake in adults aged ≥60 years or 18–64 years with at-risk medical conditions. Methods: We conducted a systematic review and meta-analysis following PRISMA and MOOSE guidelines. Searches in PubMed, EMBASE, and Scopus identified studies published in the last 10 years evaluating hospital-based interventions reporting vaccination uptake. The risk of bias was assessed using validated tools (NOS, RoB 2, ROBINS-I, QI-MQCS). A meta-analysis was conducted for categories with ≥3 eligible studies reporting pre- and post-intervention vaccination coverage in the same population. Results: We included 44 studies. Multi-component strategies (n = 21) showed the most consistent results (e.g., pneumococcal uptake from 2.2% to 43.4%, p < 0.001). Reminder-based interventions (n = 4) achieved influenza coverage increases from 31.0% to 68.0% and a COVID-19 booster uptake boost of +38% after SMS reminders. Educational strategies (n = 11) varied in effectiveness, with one study reporting influenza coverage rising from 1.6% to 12.2% (+662.5%, OR 8.86, p < 0.01). Standing order protocols increased pneumococcal vaccination from 10% to 60% in high-risk adults. Hospital-based catch-up programs improved DTaP-IPV uptake from 56.2% to 80.8% (p < 0.001). For patient education, the pooled OR was 2.11 (95% CI: 1.96–2.27; p < 0.001, I² = 97.2%) under a fixed-effects model, and 2.47 (95% CI: 1.53–3.98; p < 0.001) under a random-effects model. For multi-component strategies, the OR was 2.39 (95% CI: 2.33–2.44; p < 0.001, I² = 98.0%) with fixed effects, and 3.12 (95% CI: 2.49–3.92; p < 0.001) with random effects. No publication bias was detected. Conclusions: Hospital-based interventions, particularly those using multi-component approaches, effectively improve vaccine coverage in older and high-risk adults. Embedding vaccination into routine hospital care offers a scalable opportunity to reduce disparities and enhance population-level protection. Future policies should prioritize the institutional integration of such strategies to support healthy aging and vaccine equity.

Background: Adherence to the Mediterranean diet (MD) is associated with improved health outcomes, however limited evidence exists on the socio-demographic and behavioral determinants of MD adherence among university students, a population at risk of developing unhealthy habits during a critical life stage. Methods: A cross-sectional study was conducted among 2697 students (70.6% female) enrolled at a university in Northern Italy. MD adherence was measured using the validated Medi-Lite score. Multivariable logistic and linear regression models were used to identify socio-demographic and behavioral associations with high adherence to the MD (score ≥12). Principal component analysis was performed to explore multivariate patterns across dietary components and participant characteristics. Results: Overall, 25.6% of participants were classified as having high adherence to the MD. Higher adherence was more frequent among women, non-smokers, older students, and those living with their families. Students in health sciences showed greater adherence compared to those in other fields of study. Conversely, frequent users of mobile food ordering applications and smokers were less likely to adhere to the MD. These associations remained consistent after adjusting for age and sex. Conclusions: Adherence to the MD is suboptimal among university students and influenced by socio-demographic and behavioral factors. Targeted interventions should prioritize younger males, smokers, and convenience food users, while promoting sustainability and social support as facilitators of healthier dietary patterns.

(1) Background: Domestic violence (DV), including intimate partner violence (IPV) during pregnancy and the puerperium, represents a major public health issue, significantly affecting maternal and child health. (2) Methods: This systematic review and meta-analysis, conducted according to PRISMA 2020 guidelines, aimed to identify screening tools used to detect DV and IPV among pregnant and postpartum women and to estimate DV prevalence. The protocol was published in PROSPERO in advance (CRD42023473392). (3) Results: A comprehensive literature search across PubMed, EMBASE, Scopus, and Web of Science was conducted on 1 January 2024, resulting in 34,720 records; 98 studies met the inclusion criteria. The included studies were conducted in over 40 countries, and most were cross-sectional. Commonly used screening tools included the WHO Women’s Health and Life Experiences Questionnaire, the Abuse Assessment Screen, and the WHO Violence Against Women Instrument. Meta-analyses showed that 10% of women experienced physical violence, 26% psychological violence, 9% sexual violence, 16% verbal violence, and 13% economic violence. The overall prevalence of IPV during pregnancy and the puerperium was 26%. Despite the widespread use of validated instruments, substantial heterogeneity was observed, underscoring the need for standardization. (4) Conclusion: These findings underline the urgent need to integrate routine IPV screening into maternal care pathways using validated, culturally adapted tools, ensuring women’s safety and confidentiality.