Background: Antimicrobial resistance (AMR) has been identified as one of the top ten public health threats facing humanity. Aim: The purpose of this study was to assess the effect of an antimicrobial stewardship educational intervention on family caregivers’ knowledge and practices in primary healthcare settings in Egypt. Methods: A quasi-experimental, one-group pretest-posttest design involving a sample of 300 family caregivers attending family health centers. The data were collected using questionnaires that assessed caregiver knowledge and self-reported practices regarding AMR before and after the intervention (primary outcomes). The intervention combined tailored knowledge and practice components that carefully evaluated participants’ knowledge regarding AMR, health risk, antibiotic usage, and prevention of infection. Furthermore, their practice of using antibiotics, including previous antibiotic exposure, their antibiotic use during the past year, reasons for taking antibiotics, ways of obtaining antibiotics, and reasons for discontinuing of antibiotic therapy were also recorded. Results: The pre-intervention assessment revealed poor knowledge and practice regarding antibiotic use. Post-intervention, mean knowledge scores increased significantly from 18.36 to 23.28 (t = 19.5, p < 0.0001), while mean practice scores improved from 9.83 to 12.37 (t = 6.4, p < 0.0001). Conclusions: The intervention successfully improved caregivers’ knowledge and practices regarding AMR. However, there are some limitations that could affect the generalization, and the impact of the results such as the relatively small sample size recruited from a single center, lack of a control group, reliance on self-reported data, and lack of long-term follow-up. Future studies should aim to address these constraints in order to assess the intervention’s effectiveness.

INTRODUCTION: Artificial intelligence (AI) has the potential to improve adenoma detection rates (ADRs) during colonoscopy, but the efficacy of various AI-assisted systems remains unclear. To evaluate and compare the effectiveness of different AI-assisted systems for detecting colorectal neoplasia during colonoscopy. METHODS: A systematic literature search of PubMed, Scopus, and Google Scholar databases was conducted up to March 4, 2025, to identify randomized controlled trials comparing AI-assisted colonoscopy with conventional colonoscopy. The analysis included AI systems such as GI Genius (Medtronic, Dublin, Ireland), CAD EYE (Fujifilm, Tokyo, Japan), ENDOANGEL, EndoScreener, and EndoAID. The primary outcome was ADR, analyzed using random-effects models to calculate pooled odds ratios (OR) and 95% confidence intervals (CI). Surface under the cumulative ranking curve (SUCRA) rankings and subgroup analyses were also performed. RESULTS: Seventeen randomized controlled trials with 10,539 participants were included. ENDOANGEL showed the highest efficacy (OR 1.84, 95% CI 1.50–2.30; SUCRA 0.9), followed by EndoAID (OR 1.64, 95% CI 1.20–2.26; SUCRA 0.7). CAD EYE and GI Genius were similarly ranked (OR 1.46 and 1.45, respectively). EndoScreener was ranked just above the control group (OR 1.37, 95% CI 1.20–1.56; SUCRA 0.4). DISCUSSION: AI-assisted colonoscopy systems showed improved ADR detection rates compared with traditional colonoscopy. These results suggest that artificial intelligence may help enhance detection during colonoscopy procedures; however, additional large-scale studies are needed to confirm these findings.