AI in Clinical Research: Why NLP and LLMs Work Better Together

In clinical research, AI is no longer a future concept—it’s a core tool for making data more usable, processes more efficient, and decisions more precise. Among the most promising technologies are Natural Language Processing (NLP) and Large Language Models (LLMs). Each has distinct capabilities, and at BEKhealth, we use both.

Rather than asking “Which is better?”, the more relevant question is: Where does each work best?

This post explores the differences between NLP and LLMs, the strengths they bring to clinical workflows, and why a combined approach leads to better outcomes.

What Is NLP?

Natural Language Processing (NLP) is a branch of AI focused on analyzing and extracting meaning from human language—especially free text. In healthcare, NLP is often trained to identify structured concepts (e.g., medications, conditions, lab values) from physician notes and other unstructured medical records.

At BEKhealth, our NLP engine is built specifically for clinical data. It can:

• Extract trial-specific eligibility criteria from unstructured EMR notes
• Recognize clinical negations, temporality, and family history
• Process millions of patient records quickly and with explainable logic

NLP is ideal for high-volume, high-precision tasks where consistency and traceability are critical.

According to a 2025 study in Nature Scientific Reports, advanced NLP approaches outperformed fine-tuned LLMs in classification tasks related to mental health—highlighting the strength of focused models in clinical applications.

What are LLMs?

Large Language Models (LLMs), such as GPT or LLaMA, are trained on massive text datasets to generate human-like language. They’re generalists: able to write summaries, answer questions, and even generate new content based on a prompt.

LLMs in clinical research are commonly used to:

• Summarize long documents, such as study protocols or investigator brochures
• Assist with drafting patient-facing materials or email communications
• Answer queries or provide documentation support in conversational interfaces

LLMs shine in narrative, generative, and adaptive tasks that benefit from flexibility and fluency. However, they can sometimes produce hallucinated outputs, and their lack of explainability can limit their role in regulatory workflows.

A 2025 review in npj Digital Medicine outlined key limitations of LLMs in clinical settings, including their tendency to oversimplify or generate inaccurate medical information.

NLP and LLMs Excel in Different Areas

BEKplatform uses both NLP and LLMs where each offers the most value. Rather than forcing a single model to solve every problem, we map the right tool to the right task.

For example:

• NLP handles chart abstraction, trial feasibility, and eligibility logic.
• LLMs support communication, summarization, and surface-level interpretation.

This combined approach improves both speed and accuracy across the research lifecycle.

Comparing NLP, LLMs, and the Benefits of Using Both

Proven Benefits of BEKhealth’s NLP + LLMs Approach

At BEKhealth, our dual-engine approach allows our platform to:

• Surface 10x more clinically eligible patients
• Accelerate enrollment timelines by 2x
• Reduce chart review time for site staff
• Maintain auditability and compliance

We do this while ensuring that each AI component is working within its strengths. As a 2025 arXiv meta-analysis of over 19,000 studies confirms, LLMs and traditional NLP methods are most effective when used for their respective strengths—not as one-size-fits-all tools.

Applying the Right AI to the Right Use Case

As clinical trials grow more complex and data-rich, the demand for intelligent automation will only increase. But the real differentiator isn’t whether a solution uses LLMs or NLP—it’s how thoughtfully those technologies are applied. By using each tool for what it does best, BEKhealth ensures our platform delivers scalable, explainable, and high-impact results.