Researchers in the Chinese mainland are revolutionizing healthcare through the integration of artificial intelligence (AI) into Digital Intelligent Evidence-Based Medicine (i-EBM), a breakthrough poised to enhance precision and efficiency in medical decision-making. The innovation, detailed in a study published in the Chinese Science Bulletin, addresses long-standing challenges in traditional evidence-based medicine (EBM) while maintaining alignment with modern clinical needs.
Bridging Gaps with AI
Led by Professor Ge Long of Lanzhou University's School of Public Health, the i-EBM framework merges multi-source data integration, intelligent analysis, and personalized decision support. "AI enables rapid processing of massive datasets, transforming them into actionable treatment plans while preserving the core principles of EBM," Ge explained. This approach tackles limitations like time lags in evidence updates and insufficient individualized patient considerations.
From Theory to Clinical Practice
The system integrates electronic medical records, imaging data, and environmental factors through AI-powered knowledge graphs, enabling cross-domain analysis previously deemed impractical. In trials involving childhood pneumonia treatment, i-EBM synthesized imaging and clinical data to optimize diagnostic accuracy. Researchers have also applied the technology to traditional Chinese medicine (TCM), enhancing pattern recognition and evaluation systems for herbal formulations.
Accelerating Medical Research
AI's capacity to complete literature reviews in hours—rather than months—has already yielded practical tools, including digital guides for Chinese patent medicine usage. "Our goal is to support equitable healthcare access and elevate medical standards through human-AI collaboration," Ge emphasized, noting ongoing partnerships with medical institutions to expand i-EBM applications.
