Smart Trial: Evaluating the Use of Large Language Models for Recruiting Clinical Trial Participants via Social Media
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911152591601664 |
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| author | Zhou, Xiaofan Wang, Zisu Krieger, Janice Zalake, Mohan Cheng, Lu |
| author_facet | Zhou, Xiaofan Wang, Zisu Krieger, Janice Zalake, Mohan Cheng, Lu |
| contents | Clinical trials (CT) are essential for advancing medical research and treatment, yet efficiently recruiting eligible participants -- each of whom must meet complex eligibility criteria -- remains a significant challenge. Traditional recruitment approaches, such as advertisements or electronic health record screening within hospitals, are often time-consuming and geographically constrained. This work addresses the recruitment challenge by leveraging the vast amount of health-related information individuals share on social media platforms. With the emergence of powerful large language models (LLMs) capable of sophisticated text understanding, we pose the central research question: Can LLM-driven tools facilitate CT recruitment by identifying potential participants through their engagement on social media? To investigate this question, we introduce TRIALQA, a novel dataset comprising two social media collections from the subreddits on colon cancer and prostate cancer. Using eligibility criteria from public real-world CTs, experienced annotators are hired to annotate TRIALQA to indicate (1) whether a social media user meets a given eligibility criterion and (2) the user's stated reasons for interest in participating in CT. We benchmark seven widely used LLMs on these two prediction tasks, employing six distinct training and inference strategies. Our extensive experiments reveal that, while LLMs show considerable promise, they still face challenges in performing the complex, multi-hop reasoning needed to accurately assess eligibility criteria. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_10584 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Smart Trial: Evaluating the Use of Large Language Models for Recruiting Clinical Trial Participants via Social Media Zhou, Xiaofan Wang, Zisu Krieger, Janice Zalake, Mohan Cheng, Lu Computers and Society Artificial Intelligence Computation and Language Clinical trials (CT) are essential for advancing medical research and treatment, yet efficiently recruiting eligible participants -- each of whom must meet complex eligibility criteria -- remains a significant challenge. Traditional recruitment approaches, such as advertisements or electronic health record screening within hospitals, are often time-consuming and geographically constrained. This work addresses the recruitment challenge by leveraging the vast amount of health-related information individuals share on social media platforms. With the emergence of powerful large language models (LLMs) capable of sophisticated text understanding, we pose the central research question: Can LLM-driven tools facilitate CT recruitment by identifying potential participants through their engagement on social media? To investigate this question, we introduce TRIALQA, a novel dataset comprising two social media collections from the subreddits on colon cancer and prostate cancer. Using eligibility criteria from public real-world CTs, experienced annotators are hired to annotate TRIALQA to indicate (1) whether a social media user meets a given eligibility criterion and (2) the user's stated reasons for interest in participating in CT. We benchmark seven widely used LLMs on these two prediction tasks, employing six distinct training and inference strategies. Our extensive experiments reveal that, while LLMs show considerable promise, they still face challenges in performing the complex, multi-hop reasoning needed to accurately assess eligibility criteria. |
| title | Smart Trial: Evaluating the Use of Large Language Models for Recruiting Clinical Trial Participants via Social Media |
| topic | Computers and Society Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2509.10584 |