用NLP技术提升医学证据的获取与应用效率
Natural Language Processing in Support of Evidence-based Medicine: A Scoping Review
- 系统梳理129项NLP在循证医学中的研究，覆盖五步流程
- 助力临床决策，提升证据提取与合成效率
- 适合医疗AI、临床研究者关注前沿应用
循证医学（EBM）强调以最佳科学证据指导临床决策。由于医学文献量庞大且更新迅速，人工整理成本高昂，亟需自然语言处理（NLP）技术来识别、评估、综合、总结和传播医学证据。本综述系统分析了129项关于NLP支持循证医学的研究，阐明其在EBM五大步骤——提问（Ask）、获取（Acquire）、评估（Appraise）、应用（Apply）和评估（Assess）中的关键作用。研究揭示当前领域的局限，并提出未来方向，强调NLP在改进证据提取、合成、评估、摘要及数据可读性方面的潜力，有望显著优化临床工作流程。
原文摘要 · Abstract (English)
Evidence-based medicine (EBM) is at the forefront of modern healthcare, emphasizing the use of the best available scientific evidence to guide clinical decisions. Due to the sheer volume and rapid growth of medical literature and the high cost of curation, there is a critical need to investigate Natural Language Processing (NLP) methods to identify, appraise, synthesize, summarize, and disseminate evidence in EBM. This survey presents an in-depth review of 129 research studies on leveraging NLP for EBM, illustrating its pivotal role in enhancing clinical decision-making processes. The paper systematically explores how NLP supports the five fundamental steps of EBM -- Ask, Acquire, Appraise, Apply, and Assess. The review not only identifies current limitations within the field but also proposes directions for future research, emphasizing the potential for NLP to revolutionize EBM by refining evidence extraction, evidence synthesis, appraisal, summarization, enhancing data comprehensibility, and facilitating a more efficient clinical workflow.
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