# Smart Trial: Evaluating the Use of Large Language Models for Recruiting Clinical Trial Participants via Social Media

arXiv: https://arxiv.org/abs/2509.10584 · HTML: https://mlsanity.com/paper/2509.10584/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 43 references that are in the map.
- First posted on arXiv 12 Sep 2025.

## Builds on (most cited first, 13 of 13)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [A Deep Reinforced Model for Abstractive Summarization](https://mlsanity.com/paper/1705.04304/) (2017)
- [TRACT: Regression-Aware Fine-tuning Meets Chain-of-Thought Reasoning for LLM-as-a-Judge](https://mlsanity.com/paper/2503.04381/) (2025)
- [Robust Training for Conversational Question Answering Models with Reinforced Reformulation Generation](https://mlsanity.com/paper/2310.13505/) (2023)
- [Lighter And Better: Towards Flexible Context Adaptation For Retrieval Augmented Generation](https://mlsanity.com/paper/2409.15699/) (2024)
- [IDoFew: Intermediate Training Using Dual-Clustering in Language Models for Few Labels Text Classification](https://mlsanity.com/paper/2401.04025/) (2024)
- [Explainable CTR Prediction via LLM Reasoning](https://mlsanity.com/paper/2412.02588/) (2024)

## Abstract (arXiv)

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.