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

arXiv: https://arxiv.org/abs/2505.22280 · HTML: https://mlsanity.com/paper/2505.22280/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 155 references that are in the map.
- First posted on arXiv 28 May 2025.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [MS2: Multi-Document Summarization of Medical Studies](https://mlsanity.com/paper/2104.06486/) (2021)
- [A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature](https://mlsanity.com/paper/1806.04185/) (2018)
- [Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching](https://mlsanity.com/paper/2303.16756/) (2023)
- [Closing the gap between open-source and commercial large language models for medical evidence summarization](https://mlsanity.com/paper/2408.00588/) (2024)
- [Trial2Vec: Zero-Shot Clinical Trial Document Similarity Search using Self-Supervision](https://mlsanity.com/paper/2206.14719/) (2022)
- [Exploring the Effectiveness of Instruction Tuning in Biomedical Language Processing](https://mlsanity.com/paper/2401.00579/) (2023)
- [The Leaf Clinical Trials Corpus: a new resource for query generation from clinical trial eligibility criteria](https://mlsanity.com/paper/2207.13757/) (2022)
- [TriSum: Learning Summarization Ability from Large Language Models with Structured Rationale](https://mlsanity.com/paper/2403.10351/) (2024)
- [LeafAI: query generator for clinical cohort discovery rivaling a human programmer](https://mlsanity.com/paper/2304.06203/) (2023)
- [Automatically Summarizing Evidence from Clinical Trials: A Prototype Highlighting Current Challenges](https://mlsanity.com/paper/2303.05392/) (2023)
- [Text Classification of Cancer Clinical Trial Eligibility Criteria](https://mlsanity.com/paper/2309.07812/) (2023)
- [AlpaPICO: Extraction of PICO Frames from Clinical Trial Documents Using LLMs](https://mlsanity.com/paper/2409.09704/) (2024)
- [Validating GAN-BioBERT: A Methodology For Assessing Reporting Trends In Clinical Trials](https://mlsanity.com/paper/2106.00665/) (2021)
- [A Span-based Model for Extracting Overlapping PICO Entities from RCT Publications](https://mlsanity.com/paper/2401.06791/) (2024)

## Built on by (most cited first, 2 of 2)

- [A Disease-Aware Dual-Stage Framework for Chest X-ray Report Generation](https://mlsanity.com/paper/2511.12259/) (2025)
- [Improving Retrieval-Augmented Generation without Taxonomy-based Error Categorization](https://mlsanity.com/paper/2605.18772/) (2026)

## Abstract (arXiv)

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.