Evidence synthesis

Evidence synthesis is a core component of high-quality, patient-centered healthcare, providing a strong foundation for clinical decision-making, guideline development, and health policy. By systematically identifying, evaluating, and integrating existing research, it helps clarify the current state of evidence and identify important research gaps.

For complementary and integrative medicine, evidence synthesis is particularly important due to the heterogeneity of interventions, outcomes, and study designs. A central focus of our work is the advancement of methodological approaches to evidence synthesis, including the evidence synthesis of complex and multimodal interventions.

Within this framework, we also study the use of artificial intelligence (AI) to support and enhance evidence synthesis processes, such as literature screening, data extraction, and evidence mapping. We critically examine both the opportunities and limitations of AI-based approaches, with the goal of improving efficiency, transparency, and methodological rigor.

We place emphasis on sex and gender considerations in evidence synthesis. 

Through methodologically sound, and innovation-driven evidence synthesis, we contribute to the continued development of complementary and integrative medicine and support more personalized, inclusive, and evidence-informed healthcare.