Natural Language Processing in Support of Evidence-based Medicine: A Scoping Review
This is an incremental survey that addresses the problem of high curation costs and information overload for healthcare professionals in evidence-based medicine.
This scoping review tackles the challenge of managing the vast and growing medical literature in evidence-based medicine by surveying 129 studies on how natural language processing (NLP) methods can support its five steps, highlighting NLP's role in enhancing clinical decision-making.
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.