Evidence-Based Medicine (EBM) demands systematic changes across the healthcare system, essential for enhancing patient safety and quality of medical care. To address the question, "Are we adopting scientific methods to optimize patient safety and enhance treatment efficacy?", assessing the level of EBM implementation is crucial. The adoption rate of evidence-based medical practices varies across countries and medical fields, often being lower in resource-limited settings. In South Korea, there have been several documented cases where the adoption of non-evidence-based practices, such as CARVAR surgical procedures not based on scientific evidence, has led to severe patient safety issues, thereby raising significant concerns about the quality of medical care provided. Conversely, the ABBA Study exemplifies successful application of EBM, demonstrating how scientific research assessed the risk of intracranial hemorrhage in patients with low-dose PPA in OTC cold medicines. This study not only confirmed the associated risks but also influenced health policy, resulting in the withdrawal for PPA-containing OTC cold medicines in Korea. This positive example highlights the imperative for governments, healthcare institutions, and medical schools to expedite the transition to evidence-based, patient-centered healthcare by fostering a robust commitment to systematic reviews and enhanced support for clinical research. The Korean Society of Evidence-Based Medicine (KSEBM) is expected to play a significant role in embedding these core strategies domestically

This paper focuses on basic meta-analyses using the updated RevMan Web version, based on the Cochrane Handbook of Systematic Reviews of Interventions for clinical trials. Theoretical statistical knowledge, such as the REML method for estimating heterogeneity variance in random-effects meta-analyses, the HKSJ method for reflecting the uncertainty of pooled estimates, and the prediction interval in a random-effects model for exploring true treatment effects in a future trial, is briefly described. Examples with synthetic data are presented to help with the understanding of meta-analysts.

This paper examines some examples of not well integrating evidence into healthcare decision-making within the Republic of Korea, a nation characterized by a rapidly evolving and financially strained healthcare system. The review introduces various conceptual frameworks of evidence-based practice, including Evidence-Based Medicine (EBM), Evidence-Based Public Health (EBPH), and Evidence-Based Health Policy (EBHP), alongside a nuanced typology of scientific (context-free and context-sensitive) and colloquial evidence. Through brief literature reviews, the paper identifies significant barriers and crucial facilitators to effective evidence utilization. These include deficiencies in research infrastructure, accessibility gaps, the influence of political and value-based considerations, and the pervasive challenge of "decision-based evidence making." The report concludes by proposing actionable recommendations aimed at strengthening the evidence ecosystem, fostering deliberative processes, enhancing Health Technology Assessment (HTA) integration, and cultivating a robust culture of evidence-informed policy-making in Korea.

This review explores the current landscape of artificial intelligence (AI)-assisted semi-automation tools used in systematic reviews and guideline development. With the exponential growth of medical literature, these tools have emerged to improve efficiency and reduce the workload involved in evidence synthesis. Platforms such as Covidence, EPPI-Reviewer, DistillerSR, and Laser AI exemplify how machine learning and, more recently, large language models (LLMs) are being integrated into key stages of the systematic review process—ranging from literature screening to data extraction. Evidence suggests that these tools can save considerable time, with some achieving average reductions of over 180 hours per review. However, challenges remain in transparency, reproducibility, and validation of AI performance. In response, international initiatives such as the Responsible AI in Evidence Synthesis (RAISE) project and the Guideline International Network (GIN) have proposed frameworks to ensure the ethical, trustworthy, and effective use of AI in health research. These include principles like transparency, accountability, preplanning, and continuous evaluation. This review highlights both the opportunities and limitations of adopting AI in evidence synthesis and underscores the importance of human oversight and rigorous validation to ensure that such tools enhance, rather than compromise, the integrity of systematic reviews and guideline development.