Design and methodology of the AI-empowered Clinical Evidence for Integrated Chinese-Western Medicine (ACE-iMed) platform
Hui Liu, Ke Xu, Jie Zhang, Shouyuan Wu, Yishan Qin, Yanfang Ma, Xuan Yu, Huayu Zhang, Haodong Li, Meihua Wu, Zijing Wang, Xufei Luo, Bingyi Wang, Yuanyuan Yao, Yandong Feng, Luyuan Sun, Mengyue Dong, 洪英杰, Jiayi Liu, Rui Yang, Yiming Hu, Honghao Lai, Qi Zhou, Xuefeng Li, Long Ge, Yaolong Chen, Zhaoxiang Bian
Abstract
Background: Integrated Chinese-Western medicine (ICWM) is a distinctive medical system that plays an important role in healthcare and has received increasing attention in recent years. To facilitate the dissemination of evidence in ICWM, we developed an Artificial Intelligence (AI)-empowered Clinical Evidence for Integrated Chinese-Western Medicine (ACE-iMed) platform. Methods: A multidisciplinary working group was established, including individuals with professional backgrounds in evidence-based medicine methodology, Chinese medicine (CM), Western medicine (WM), and ICWM clinical practice and research, and computer science. Through multiple rounds of discussions, the working group defined the framework and methodology of the platform, and then applied the platform to summarize evidence for eight diseases. Results: The ACE-iMed platform (website: www.aceimed.org) contains two interfaces. The first enables the developers to store and screen the literature, perform methodological quality assessments, and generate evidence summaries. The AI-empowered workflows showed good consistency and stability across multiple stages, including literature screening and assessment of risk of bias/methodological quality, and effectively support summarizing evidence for eight diseases. The second interface, intended for end users, provides synchronized access to the included literature and the generated summaries, enabling quick access to clinical question-oriented evidence resources. Conclusion: This study introduces an AI-empowered, clinical question-oriented ICWM evidence platform. Application across eight diseases demonstrated the platform's feasibility and practical utility. The platform not only supports the developers in summarizing evidence but also provides end users with a potential pathway to access evidence and its summaries.