Natural language processing revolutionizes evidence-based healthcare delivery

Natural language processing is fast becoming the backbone of evidence-based medicine, offering new tools to handle the volume, complexity, and velocity of clinical research needed to support modern medical decision-making.

A new scoping review titled "Natural Language Processing in Support of Evidence-Based Medicine: A Scoping Review", published on arXiv in May 2025, provides a detailed examination of how NLP is shaping every core step of evidence-based medicine (EBM). Authored by Zihan Xu, Haotian Ma, Gongbo Zhang, Yihao Ding, Chunhua Weng, and Yifan Peng, the study analyzes 129 papers published between 2019 and 2024. It identifies how NLP tools are being implemented across the five pillars of EBM - Ask, Acquire, Appraise, Apply, and Assess - and pinpoints major opportunities and limitations in scaling their use across real-world clinical practice.

How is NLP reinventing each step of the EBM cycle?

The study maps NLP research efforts to the EBM workflow, starting with the way clinical questions are asked and ending with the application of findings in patient care. Transformer models like BioBERT are enhancing search query interpretation in the Ask phase. For Acquire, named entity recognition and relation extraction help extract structured elements such as population, intervention, comparison, and outcome (PICO), improving evidence retrieval from clinical trials and guidelines.

In the Appraise stage, deep learning models assess study credibility and rank evidence quality. NLP tools like SciBERT and BlueBERT are automating evidence screening in systematic reviews. For Synthesis, neural summarizers such as TrialsSummarizer and EvidenceMap use structured abstractions and retrieval-augmented generation to deliver real-time, tailored summaries of evidence.

The Apply and Assess stages benefit from tools like TrialGPT and AutoTrial, which automate clinical trial matching and eligibility criteria generation. These models connect literature with electronic health record data to streamline patient-trial alignment and drug repurposing decisions. The review shows a growing trend in using question-answering systems to support real-time clinical decisions at the point of care.

What are the key technologies and clinical domains leading NLP innovation?

Cardiology and oncology dominate as application domains, with systems like Watson Oncology Literature Insights and Clinical Trial Matching achieving accuracy rates above 90% for matching patients to appropriate studies. NLP also plays a crucial role in drug repurposing efforts, with tools like CovidX used to scan COVID-19 literature for candidate treatments.

The underlying NLP technologies include both rule-based and statistical approaches, but most of the recent progress relies on transformer-based large language models such as GPT-4, SciBERT, and domain-specific models like PubMedBERT and SAPBERT. In particular, generative language models are emerging as core engines for evidence summarization, question answering, and synthesis.

One emerging frontier is using retrieval-augmented generation (RAG) to pair generative power with factual reliability. Question-answering systems like SOPHIA and TrialGPT are already capable of supporting eligibility and treatment queries by aligning text output with verified biomedical sources.

What barriers remain for NLP in clinical integration?

Despite significant progress, the review identifies six persistent challenges. First, generative models suffer from hallucination and lack of source transparency, which limits their clinical reliability. Second, there are insufficient benchmark datasets for synthesis and appraisal tasks across many specialties.

Third, while few-shot learning could help in rare or underrepresented diseases, its use in clinical NLP remains underdeveloped. Fourth, many existing systems lack interpretability, raising concerns about bias and safety in real-world use. Fifth, NLP models often operate in silos and must be integrated with real-world data, including genomics, mHealth apps, and population-level databases - for broader impact.

Lastly, scalability across specialties remains limited. Most NLP systems are built for single-disease contexts, and few can handle comorbidities or overlapping trials. The review calls for scalable, modular architectures and robust multi-specialty datasets to support generalizability.