Natural Language Processing in Support of Evidence-based Medicine: A Scoping Review

2025-05-28

AI Summary

Facing the explosive growth of medical literature and the high cost of manual evidence-based practice (EBP), this study systematically reviews 129 works to establish, for the first time, a comprehensive NLP-enabled framework for evidence-based medicine (EBM), spanning the five core EBM stages: asking clinical questions, acquiring evidence, appraising evidence quality, applying findings to practice, and assessing outcomes. To address the heterogeneity and domain specificity of medical text, we integrate techniques including text classification, relation extraction, abstractive summarization, evidence-level classification, and clinical guideline structuring. We identify performance ceilings and generalization bottlenecks of prevailing methods, and propose a novel “evidence extraction–synthesis–interpretability” co-optimization paradigm. Furthermore, we design a cross-institutional collaborative annotation protocol and a domain-adaptive evaluation benchmark. This work delivers a practical, implementation-oriented roadmap for developing clinically deployable NLP systems in EBM.

Technology Category

• Natural Language Processing: Information Extraction
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Multiagent Systems: Mechanism Design

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

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.

Problem

• NLP methods for identifying and summarizing medical evidence
• Enhancing clinical decision-making through NLP in EBM
• Addressing limitations and future research in NLP for EBM

Innovation

• NLP enhances evidence extraction and synthesis
• NLP improves clinical decision-making processes
• NLP supports five fundamental EBM steps