Improving Clinical Trial Recruitment using Clinical Narratives and Large Language Models

• URL: http://arxiv.org/abs/2604.05190v1
• Date: Mon, 06 Apr 2026 21:37:05 GMT
• Title: Improving Clinical Trial Recruitment using Clinical Narratives and Large Language Models
• Abstract summary: Screening patients for enrollment is a labor-intensive bottleneck that leads to under-enrollment and, ultimately, trial failures.<n>Recent breakthroughs in large language models (LLMs) offer a promising opportunity to use artificial intelligence to improve screening.<n>This study systematically explored both encoder- and decoder-based generative LLMs for screening clinical narratives to facilitate clinical trial recruitment.
• Score: 11.512193481146122
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Screening patients for enrollment is a well-known, labor-intensive bottleneck that leads to under-enrollment and, ultimately, trial failures. Recent breakthroughs in large language models (LLMs) offer a promising opportunity to use artificial intelligence to improve screening. This study systematically explored both encoder- and decoder-based generative LLMs for screening clinical narratives to facilitate clinical trial recruitment. We examined both general-purpose LLMs and medical-adapted LLMs and explored three strategies to alleviate the "Lost in the Middle" issue when handling long documents, including 1) Original long-context: using the default context windows of LLMs, 2) NER-based extractive summarization: converting the long document into summarizations using named entity recognition, 3) RAG: dynamic evidence retrieval based on eligibility criteria. The 2018 N2C2 Track 1 benchmark dataset is used for evaluation. Our experimental results show that the MedGemma model with the RAG strategy achieved the best micro-F1 score of 89.05%, outperforming other models. Generative LLMs have remarkably improved trial criteria that require long-term reasoning across long documents, whereas trial criteria that span a short piece of context (e.g., lab tests) show incremental improvements. The real-world adoption of LLMs for trial recruitment must consider specific criteria for selecting among rule-based queries, encoder-based LLMs, and generative LLMs to maximize efficiency within reasonable computing costs.

Related papers

• LLM4CKD: Large Language Models for Early Stage Chronic Kidney Disease Screening [0.5442955439283729]
  Early screening of chronic kidney disease (CKD) is critical for timely intervention.<n>Most machine learning (ML) and deep learning (DL) approaches require labeled data and model training.<n>This study evaluates the effectiveness of large language models (LLMs) for CKD screening under zero-shot and few-shot in-context learning settings.
  arXiv  Detail & Related papers  (2026-09-03T15:53:20Z)
• Deliberate Evolution: Agentic Reasoning for Sample-Efficient Symbolic Regression with LLMs [52.89778838903305]
  Deliberate Evolution (DE) is an agentic framework that decouples symbolic generation from search control.<n>Experiments on LLM-SRBench show that DE consistently outperforms representative LLM-based SR baselines across diverse scientific domains.
  arXiv  Detail & Related papers  (2026-06-03T02:22:16Z)
• PREBA: Surgical Duration Prediction via PCA-Weighted Retrieval-Augmented LLMs and Bayesian Averaging Aggregation [51.96735866702332]
  PREBA is a retrieval-augmented framework that integrates PCA-weighted retrieval and Bayesian averaging aggregation.<n> PREBA significantly improves performance-for instance, reducing MAE by up to 40% and raising R2 from -0.13 to 0.62 over zero-shot inference.
  arXiv  Detail & Related papers  (2026-02-27T07:19:23Z)
• A Federated and Parameter-Efficient Framework for Large Language Model Training in Medicine [59.78991974851707]
  Large language models (LLMs) have demonstrated strong performance on medical benchmarks, including question answering and diagnosis.<n>Most medical LLMs are trained on data from a single institution, which faces limitations in generalizability and safety in heterogeneous systems.<n>We introduce the model-agnostic and parameter-efficient federated learning framework for adapting LLMs to medical applications.
  arXiv  Detail & Related papers  (2026-01-29T18:48:21Z)
• Rethinking On-policy Optimization for Query Augmentation [49.87723664806526]
  We present the first systematic comparison of prompting-based and RL-based query augmentation across diverse benchmarks.<n>We introduce a novel hybrid method, On-policy Pseudo-document Query Expansion (OPQE), which learns to generate a pseudo-document that maximizes retrieval performance.
  arXiv  Detail & Related papers  (2025-10-20T04:16:28Z)
• CliniBench: A Clinical Outcome Prediction Benchmark for Generative and Encoder-Based Language Models [39.221038061767324]
  generative large language models (LLMs) are being increasingly investigated for complex medical tasks.<n>Their effectiveness in real-world clinical applications remains underexplored.<n>We present CliniBench, the first benchmark that enables comparisons of encoder-based classifiers and generative LLMs for discharge diagnosis prediction from admission notes in MIMIC-IV dataset.
  arXiv  Detail & Related papers  (2025-09-30T11:56:53Z)
• Cohort Discovery: A Survey on LLM-Assisted Clinical Trial Recruitment [21.800829039344876]
  We are the first to analyze the task of trial-patient matching and contextualize emerging LLM-based approaches in clinical trial recruitment.<n>We critically examine existing benchmarks, approaches and evaluation frameworks.
  arXiv  Detail & Related papers  (2025-06-18T09:32:16Z)
• Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers [74.17516978246152]
  Large language models (LLMs) have been widely integrated into information retrieval to advance traditional techniques.<n>We propose EXSEARCH, an agentic search framework, where the LLM learns to retrieve useful information as the reasoning unfolds.<n>Experiments on four knowledge-intensive benchmarks show that EXSEARCH substantially outperforms baselines.
  arXiv  Detail & Related papers  (2025-05-26T15:27:55Z)
• AutoElicit: Using Large Language Models for Expert Prior Elicitation in Predictive Modelling [53.54623137152208]
  We introduce AutoElicit to extract knowledge from large language models and construct priors for predictive models.<n>We show these priors are informative and can be refined using natural language.<n>We find that AutoElicit yields priors that can substantially reduce error over uninformative priors, using fewer labels, and consistently outperform in-context learning.
  arXiv  Detail & Related papers  (2024-11-26T10:13:39Z)
• Leveraging Large Language Models for Medical Information Extraction and Query Generation [2.1793134762413433]
  This paper introduces a system that integrates large language models (LLMs) into the clinical trial retrieval process. We evaluate six LLMs for query generation, focusing on open-source and relatively small models that require minimal computational resources.
  arXiv  Detail & Related papers  (2024-10-31T12:01:51Z)
• From RAGs to riches: Utilizing large language models to write documents for clinical trials [0.0]
  This article has now been published on journal website: https://journals.sagepub.com/doi177/17407745251320806.<n>Pubmed link: https://pubmed.ncbi.nlm.nih.gov/40013826/.
  arXiv  Detail & Related papers  (2024-02-26T08:59:05Z)
• Large Language Model Distilling Medication Recommendation Model [58.94186280631342]
  We harness the powerful semantic comprehension and input-agnostic characteristics of Large Language Models (LLMs)<n>Our research aims to transform existing medication recommendation methodologies using LLMs.<n>To mitigate this, we have developed a feature-level knowledge distillation technique, which transfers the LLM's proficiency to a more compact model.
  arXiv  Detail & Related papers  (2024-02-05T08:25:22Z)
• Distilling Large Language Models for Matching Patients to Clinical Trials [3.4068841624198942]
  The recent success of large language models (LLMs) has paved the way for their adoption in the high-stakes domain of healthcare. This study presents the first systematic examination of the efficacy of both proprietary (GPT-3.5, and GPT-4) and open-source LLMs (LLAMA 7B,13B, and 70B) for the task of patient-trial matching. Our findings reveal that open-source LLMs, when fine-tuned on this limited and synthetic dataset, demonstrate performance parity with their proprietary counterparts.
  arXiv  Detail & Related papers  (2023-12-15T17:11:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.