# Controlled LLM-based Reasoning for Clinical Trial Retrieval

arXiv: https://arxiv.org/abs/2409.18998 · HTML: https://mlsanity.com/paper/2409.18998/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 19 Sep 2024.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)

## Builds on (most cited first, 3 of 3)

- [Language models are not naysayers: An analysis of language models on negation benchmarks](https://mlsanity.com/paper/2306.08189/) (2023)
- [Effective Matching of Patients to Clinical Trials using Entity Extraction and Neural Re-ranking](https://mlsanity.com/paper/2307.00381/) (2023)
- [Utilizing ChatGPT to Enhance Clinical Trial Enrollment](https://mlsanity.com/paper/2306.02077/) (2023)

## Built on by (most cited first, 3 of 3)

- [A Survey on LLM-Assisted Clinical Trial Recruitment](https://mlsanity.com/paper/2506.15301/) (2025)
- [A systematic review of trial-matching pipelines using large language models](https://mlsanity.com/paper/2509.19327/) (2025)
- [AbstRAG: Learning to Abstract for Retrieval Problems](https://mlsanity.com/paper/2606.09459/) (2026)

## Abstract (arXiv)

Matching patients to clinical trials demands a systematic and reasoned interpretation of documents which require significant expert-level background knowledge, over a complex set of well-defined eligibility criteria. Moreover, this interpretation process needs to operate at scale, over vast knowledge bases of trials. In this paper, we propose a scalable method that extends the capabilities of LLMs in the direction of systematizing the reasoning over sets of medical eligibility criteria, evaluating it in the context of real-world cases. The proposed method overlays a Set-guided reasoning method for LLMs. The proposed framework is evaluated on TREC 2022 Clinical Trials, achieving results superior to the state-of-the-art: NDCG@10 of 0.693 and Precision@10 of 0.73.