Academic

Improving Clinical Trial Recruitment using Clinical Narratives and Large Language Models

arXiv:2604.05190v1 Announce Type: new 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 r

Z
Ziyi Chen, Mengxian Lyu, Cheng Peng, Yonghui Wu · · 1 min read · 14 views
#cs.CL #cs.AI #cs.IR

arXiv:2604.05190v1 Announce Type: new 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.

Executive Summary

The study evaluates the efficacy of large language models (LLMs) in enhancing clinical trial recruitment by screening patient narratives. Using the 2018 N2C2 Track 1 benchmark dataset, the authors compare encoder- and decoder-based LLMs, including general-purpose and medical-adapted models, under three strategies to mitigate the 'Lost in the Middle' problem in long documents: original long-context, NER-based extractive summarization, and retrieval-augmented generation (RAG). The MedGemma model with RAG achieved the highest micro-F1 score of 89.05%, demonstrating significant improvements in handling criteria requiring long-term reasoning. The findings underscore the potential of LLMs to streamline trial recruitment, particularly for complex eligibility criteria, while highlighting the need for strategic selection among rule-based, encoder-based, and generative LLMs to balance efficiency and computational costs.

Key Points

  • ▸ LLMs, particularly medical-adapted models like MedGemma, significantly enhance clinical trial recruitment by improving patient screening from clinical narratives.
  • ▸ The study evaluates three strategies—long-context, NER-based summarization, and RAG—to address challenges in processing long documents, with RAG proving most effective for complex eligibility criteria.
  • ▸ Performance varies by trial criteria complexity; generative LLMs excel in long-term reasoning tasks, while rule-based or encoder-based models may suffice for simpler criteria, balancing efficiency and cost.
  • ▸ The 2018 N2C2 Track 1 dataset provides a robust benchmark for evaluating LLM performance in clinical trial recruitment.
  • ▸ Real-world adoption requires careful consideration of cost, computational efficiency, and the specific nature of eligibility criteria to optimize LLM deployment.

Merits

Novelty and Rigor

The study pioneers the systematic comparison of encoder- and decoder-based LLMs, including medical-adapted models, for clinical trial recruitment, employing a robust benchmark dataset (N2C2 Track 1) and three distinct strategies to address long-document challenges.

Practical Utility

The findings offer actionable insights for healthcare institutions and trial organizers, demonstrating that RAG-enhanced LLMs can substantially improve screening efficiency, particularly for complex eligibility criteria, thereby reducing recruitment bottlenecks and trial failures.

Methodological Innovation

The exploration of RAG as a dynamic evidence retrieval mechanism represents a forward-looking approach to handling long clinical narratives, addressing the 'Lost in the Middle' problem with precision and scalability.

Demerits

Dataset Limitations

The reliance on the 2018 N2C2 Track 1 dataset may not fully capture the diversity and complexity of modern clinical narratives or evolving trial recruitment challenges, potentially limiting generalizability.

Computational Costs

The use of advanced LLMs, particularly decoder-based models with RAG, incurs significant computational costs, which may pose barriers to adoption for smaller institutions or resource-constrained healthcare systems.

Criteria Specificity

The study highlights variable performance improvements across different trial criteria, suggesting that not all eligibility criteria benefit equally from generative LLMs, which may limit their universal applicability without tailored strategies.

Expert Commentary

This study represents a significant leap forward in leveraging AI to address the persistent challenge of under-enrollment in clinical trials, a critical bottleneck that undermines the development of life-saving therapies. The systematic evaluation of LLMs, particularly the superior performance of MedGemma with RAG, underscores the transformative potential of generative AI in healthcare. However, the authors’ emphasis on the variability in performance across different trial criteria is a nuanced insight that reinforces the need for a tailored approach—one that aligns technical capabilities with clinical and operational realities. The study also subtly highlights the tension between innovation and accessibility; while the results are promising, the computational demands of such models may inadvertently widen the gap between well-resourced academic centers and smaller healthcare providers. Moreover, the ethical dimensions of AI-driven recruitment cannot be overstated. As LLMs assume a greater role in patient screening, ensuring fairness, transparency, and accountability becomes paramount. The authors’ call for strategic selection among rule-based, encoder-based, and generative LLMs is a pragmatic response to these challenges, but it also signals a broader imperative: the need for interdisciplinary collaboration among clinicians, data scientists, ethicists, and policymakers to harness AI’s potential responsibly. This work is not merely an academic exercise; it is a blueprint for the future of clinical trial recruitment, demanding both enthusiasm and caution in equal measure.

Recommendations

  • ✓ Conduct further research to validate the findings across diverse clinical settings and datasets, including longitudinal studies to assess the impact of LLMs on trial outcomes and participant diversity.
  • ✓ Develop standardized protocols for the ethical deployment of LLMs in clinical trial recruitment, including bias audits, transparency reports, and patient consent frameworks to mitigate potential harms.
  • ✓ Invest in scalable infrastructure and partnerships to lower the barriers to adoption of advanced LLMs, particularly for smaller institutions, and ensure interoperability with existing EHR systems.
  • ✓ Establish cross-disciplinary task forces comprising clinicians, AI researchers, ethicists, and regulators to oversee the integration of LLMs into clinical trial processes, ensuring alignment with regulatory standards and ethical best practices.
  • ✓ Explore the potential of federated learning and privacy-preserving AI techniques to enhance data security and patient privacy while enabling the training and deployment of LLMs in multi-institutional settings.

Sources

Original: arXiv - cs.CL

Related Articles