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

TL;DR

This paper reviews 129 studies on how Natural Language Processing techniques support evidence-based medicine by improving evidence extraction, synthesis, and clinical decision-making, highlighting current limitations and future directions.

Contribution

It provides a comprehensive scoping review of NLP applications in EBM, systematically analyzing their roles across all five steps of evidence-based practice and identifying research gaps.

Findings

NLP enhances evidence identification and synthesis in EBM.

Current limitations include data quality and integration challenges.

Future research should focus on improving NLP accuracy and clinical workflow integration.

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.