@inproceedings{ghosh-etal-2025-survey-llm,
title = "A Survey on {LLM}-Assisted Clinical Trial Recruitment",
author = "Ghosh, Shrestha and
Schneider, Moritz and
Reinicke, Carina and
Eickhoff, Carsten",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.ijcnlp-long.35/",
pages = "625--646",
ISBN = "979-8-89176-298-5",
abstract = "Clinical trials are designed in natural language and the task of matching them to patients, represented via both structured and unstructured textual data, benefits from knowledge aggregation and reasoning abilities of LLMs. LLMs with their ability to consolidate distributed knowledge hold the potential to build a more general solution than classical approaches that employ trial-specific heuristics. Yet, adoption of LLMs in critical domains, such as clinical research, comes with many challenges, such as, the availability of public benchmarks, the dimensions of evaluation and data sensitivity. In this survey, we contextualize emerging LLM-based approaches in clinical trial recruitment. We examine the main components of the clinical trial recruitment process, discuss existing challenges in adopting LLM technologies in clinical research and exciting future directions."
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%0 Conference Proceedings
%T A Survey on LLM-Assisted Clinical Trial Recruitment
%A Ghosh, Shrestha
%A Schneider, Moritz
%A Reinicke, Carina
%A Eickhoff, Carsten
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-298-5
%F ghosh-etal-2025-survey-llm
%X Clinical trials are designed in natural language and the task of matching them to patients, represented via both structured and unstructured textual data, benefits from knowledge aggregation and reasoning abilities of LLMs. LLMs with their ability to consolidate distributed knowledge hold the potential to build a more general solution than classical approaches that employ trial-specific heuristics. Yet, adoption of LLMs in critical domains, such as clinical research, comes with many challenges, such as, the availability of public benchmarks, the dimensions of evaluation and data sensitivity. In this survey, we contextualize emerging LLM-based approaches in clinical trial recruitment. We examine the main components of the clinical trial recruitment process, discuss existing challenges in adopting LLM technologies in clinical research and exciting future directions.
%U https://aclanthology.org/2025.ijcnlp-long.35/
%P 625-646
Markdown (Informal)
[A Survey on LLM-Assisted Clinical Trial Recruitment](https://aclanthology.org/2025.ijcnlp-long.35/) (Ghosh et al., IJCNLP-AACL 2025)
ACL
- Shrestha Ghosh, Moritz Schneider, Carina Reinicke, and Carsten Eickhoff. 2025. A Survey on LLM-Assisted Clinical Trial Recruitment. In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 625–646, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.