ystematic reviews in the era of artificial intelligence: Opportunities, challenges, and the future of evidence synthesis

Abstract

Systematic reviews remain the cornerstone of evidence-based practice, providing a rigorous and transparent synthesis of research findings across diverse disciplines. However, the exponential growth of scientific literature has placed considerable pressure on traditional review methodologies, which are increasingly constrained by time, resources, and human capacity. In response, artificial intelligence (AI) has emerged as a transformative force in evidence synthesis, offering new opportunities to enhance the efficiency, accuracy, and scalability of systematic reviews. This article critically examines the integration of AI into the systematic review process, with particular attention to its applications across key stages, including literature searching, screening, data extraction, quality appraisal, and evidence synthesis. It further explores the benefits of AI, such as reduced workload, improved consistency, and accelerated review timelines, while also addressing important limitations, including algorithmic bias, lack of transparency, reproducibility concerns, and ethical challenges. In addition, the article considers the evolving roles of researchers and academic journals in adapting to AI-driven methodologies, emphasising the need for new competencies, reporting standards, and editorial guidelines. The article concludes that the future of systematic reviews lies in a hybrid model in which human expertise and artificial intelligence collaborate to strengthen the rigor, responsiveness, and impact of evidence synthesis in the digital age.