SmartEBM AI Agent: A Web-Based Platform for Streamlining Network Meta-Analysis via Human-AI Collaboration

Network meta-analysis (NMA) plays an important role in comparative effectiveness research, particularly in fields such as traditional and complementary medicine, where multiple interventions often need to be assessed within a single evidence framework. However, conducting NMA remains labor-intensive, methodologically demanding, and difficult to complete efficiently using conventional workflows. Although recent advances in large language models have created new opportunities for supporting evidence synthesis, their routine use in NMA is still constrained by limited transparency, prompt dependency, and difficulty integrating with structured analytical procedures.

We developed SmartEBM, a web-based human-AI collaborative platform designed to support the full workflow of NMA. The platform is organized around a human-in-the-loop model, in which AI-assisted functions support repeated and labor-intensive tasks while researchers retain oversight of methodological judgment, verification, and final decision-making. SmartEBM integrates six functional modules: title and abstract screening, full-text screening, data extraction, risk of bias assessment, statistical analysis, and certainty of evidence assessment.

SmartEBM provides low-code interfaces, structured outputs, and verification-oriented workspaces that connect the major steps of evidence synthesis within a single platform. The six modules collectively address the core analytical stages of NMA, enabling researchers to conduct screening, extraction, assessment, and analysis in an integrated environment rather than across disparate tools.

Rather than functioning as a stand-alone automation tool, SmartEBM is intended as a practical platform for end-to-end NMA support. This platform-oriented approach may help make evidence synthesis more manageable, traceable, and accessible in routine research practice, especially in complex review settings.