Introduction: Infection control in intensive care units (ICUs) is crucial due to the high risk of healthcare‐associated infections (HAIs), which can increase patient morbidity, mortality, and costs. Effective measures such as hand hygiene, use of personal protective equipment (PPE), patient isolation, and environmental cleaning are vital to minimize these risks. The integration of artificial intelligence (AI) offers new opportunities to enhance infection control, from predicting outbreaks to optimizing antimicrobial use, ultimately improving patient safety and care in ICUs. Objectives: The primary objectives are to explore AI's impact on predicting HAIs, real‐time monitoring, automated sterilization, resource optimization, and personalized infection control plans. Methodology: A comprehensive search of PubMed and Scopus was conducted for relevant articles up to January 2024, including case series, reports, and cohort studies. Animal studies and irrelevant articles were excluded, with a focus on those considered to have significant clinical relevance. Discussion: The review highlights AI's prowess in predicting HAIs, surpassing conventional methods. Existing evidence demonstrates AI's efficacy in accurately predicting and mitigating HAIs. Real‐time patient monitoring and alert systems powered by AI are shown to enhance infection detection and patient outcomes. The paper also addresses AI's role in automating sterilization and disinfection, with studies affirming its effectiveness in reducing infections. AI's resource optimization capabilities are exemplified in ICU settings, showcasing its potential to improve resource allocation efficiency. Furthermore, the review emphasizes AI's personalized approach to infection control post‐procedures, elucidating its ability to analyze patient data and create tailored control plans.

Background: Chemotherapy-induced cardiac dysfunction (CIC) is a significant and concerning complication observed among cancer patients. Despite the demonstrated cardioprotective benefits of statins in various cardiovascular diseases, their effectiveness in mitigating CIC remains uncertain. Objective: This meta-analysis aims to comprehensively evaluate the potential cardioprotective role of statins in patients with CIC. Methods: A systematic literature search was conducted using PubMed, Embase, and Scopus databases to identify relevant articles published from inception until 10th May 2023. The outcomes were assessed using pooled odds ratio (OR) for categorical data and mean difference (MD) for continuous data, with corresponding 95% confidence intervals (95% CIs). Results: This meta-analysis comprised nine studies involving a total of 5532 patients, with 1904 in the statin group and 3628 in the non-statin group. The pooled analysis of primary outcome shows that patients who did not receive statin suffer a greater decline in the LVEF after chemotherapy compared to those who receive statin (MD, 3.55 (95% CI: 1.04–6.05), p = 0.01). Likewise, we observed a significantly higher final mean LVEF among chemotherapy patients with statin compared to the non-statin group of patients (MD, 2.08 (95% CI: 0.86–3.30), p > 0.001). Additionally, there was a lower risk of incident heart failure in the statin group compared to the non-statin group of patients (OR, 0.41 (95% CI: 0.27–0.62), p < 0.001). Lastly, the change in the mean difference for LVEDV was not statistically significant between the statin and non-statin groups (MD, 1.55 (95% CI: −5.22–8.33), p = 0.65). Conclusion: Among patients of CIC, statin use has shown cardioprotective benefits by improving left ventricular function and reducing the risk of heart failure.