CAMIH – The Complementary and Alternative Medicine Insights Hub

Introduction: Assessing the ever-growing number of publications in evidence-based medicine by means of their risk of biases is as essential as it is challenging. This is especially true for the field of complementary and alternative medicine (CAM), a field that remains underrepresented in systematic review collections such as those by the Cochrane Review Groups. Methods: In this work, we present CAMIH, a semantic wiki platform that offers clinicians a collaborative space to find, summarize, and discuss CAM evidence. CAMIH is built on semantic web technologies and structures information using semantic triplets. By structuring like this, CAMIH goes beyond simple data collection. Our goal is to enable a deeper understanding and organization of evidence, thereby acting as a CAM-specific supplement to existing evidence-synthesis frameworks inspired by the Cochrane methodology. Results: We anticipate the implemented platform to make evidence synthesis and risk of bias assessment more efficient, but also reduce the time required to derive treatment strategies. Given its foundation in semantic web technologies, it serves both as a practical tool for clinicians and as a methodological blueprint for other research domains seeking to systematically organize gathered evidence. Discussion: Given the advantages of the platform, it requires, in its current state, manual efforts to be kept up to date. However, our goal is too semi-automize this process to sustainably keep CAMIH relevant. Conclusion: This work provides an addition to the evidence database-landscape for the CAM field. We hope it will enable clinicians to create, discuss, and synthesize evidence while also providing a blueprint for other research areas that want to organize evidence.