Papers with RCTs
Towards Understanding of Medical Randomized Controlled Trials by Conclusion Generation (D19-62)
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| Challenge: | Using machine learning to interpret large amounts of data can be over-whelming for clinicians. |
| Approach: | They propose to use PubMed 200k RCT sentence classification dataset to generate RCT conclusion generation task. |
| Outcome: | The proposed model improves quality and correctness in generated conclusions compared to baseline model . the proposed model is not suitable for all RCTs, but it could be improved . |
RoBGuard: Enhancing LLMs to Assess Risk of Bias in Clinical Trial Documents (2025.coling-main)
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Changkai Ji, Bowen Zhao, Zhuoyao Wang, Yingwen Wang, Yuejie Zhang, Ying Cheng, Rui Feng, Xiaobo Zhang
| Challenge: | Existing approaches to assess the risk of bias in RCTs focus on manually crafted prompts and a restricted set of simple questions, limiting their accuracy and generalizability. |
| Approach: | They propose a framework for enhancing Large Language Models to assess the risk of bias in RCTs by reformulation, document parsing and multi-expert collaboration. |
| Outcome: | The proposed framework outperforms existing methods on the RoB-Item and RoB domains. |
Leveraging Large Language Models for Learning Complex Legal Concepts through Storytelling (2024.acl-long)
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Hang Jiang, Xiajie Zhang, Robert Mahari, Daniel Kessler, Eric Ma, Tal August, Irene Li, Alex Pentland, Yoon Kim, Deb Roy, Jad Kabbara
| Challenge: | a novel application of large language models (LLMs) to legal education helps non-experts learn complex legal concepts . authors find storytelling helps nonexperts understand complex legal terms and concepts compared to definitions . |
| Approach: | They propose a novel application of large language models to legal education . they use LLMs to generate legal stories explaining complex legal concepts . |
| Outcome: | The proposed method improves comprehension and interest among non-native speakers compared to definitions . the novel method also shows that non-experts retain more stories . |
FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence (2024.acl-long)
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Sebastian Joseph, Lily Chen, Jan Trienes, Hannah Göke, Monika Coers, Wei Xu, Byron Wallace, Junyi Jessy Li
| Challenge: | FactPICO is a factuality benchmark for plain language summarization of medical texts describing randomized controlled trials . existing metrics for factual summarizing medical evidence are poorly correlated with expert judgments on the instance level. |
| Approach: | They propose a factuality benchmark for plain language summarization of medical texts . they assess factuality of critical elements of RCTs in those summaries . |
| Outcome: | The proposed benchmark assesses the factuality of medical summaries using LLMs . the summary summators are based on 345 plain language summaires with fine-grained evaluation . |