# Trialstreamer: Mapping and Browsing Medical Evidence in Real-Time

arXiv: https://arxiv.org/abs/2005.10865 · HTML: https://mlsanity.com/paper/2005.10865/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2020.

## Built on by (most cited first, 12 of 12)

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- [Scaling Clinical Trial Matching Using Large Language Models: A Case Study in Oncology](https://mlsanity.com/paper/2308.02180/) (2023)
- [FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence](https://mlsanity.com/paper/2402.11456/) (2024)
- [Automatically Summarizing Evidence from Clinical Trials: A Prototype Highlighting Current Challenges](https://mlsanity.com/paper/2303.05392/) (2023)
- [Artificial Intelligence for In Silico Clinical Trials: A Review](https://mlsanity.com/paper/2209.09023/) (2022)
- [Clinical Evidence Engine: Proof-of-Concept For A Clinical-Domain-Agnostic Decision Support Infrastructure](https://mlsanity.com/paper/2111.00621/) (2021)
- [Automated tabulation of clinical trial results: A joint entity and relation extraction approach with transformer-based language representations](https://mlsanity.com/paper/2112.05596/) (2021)
- [SciDaSynth: Interactive Structured Data Extraction from Scientific Literature with Large Language Model](https://mlsanity.com/paper/2404.13765/) (2024)
- [Query-driven Document-level Scientific Evidence Extraction from Biomedical Studies](https://mlsanity.com/paper/2505.06186/) (2025)
- [Impact of detecting clinical trial elements in exploration of COVID-19 literature](https://mlsanity.com/paper/2105.12261/) (2021)
- [EviStreams: Human-in-the-Loop AI Data Extraction for Systematic Reviews in Medicine](https://mlsanity.com/paper/2609.27418/) (2026)

## Abstract (arXiv)

We introduce Trialstreamer, a living database of clinical trial reports. Here we mainly describe the evidence extraction component; this extracts from biomedical abstracts key pieces of information that clinicians need when appraising the literature, and also the relations between these. Specifically, the system extracts descriptions of trial participants, the treatments compared in each arm (the interventions), and which outcomes were measured. The system then attempts to infer which interventions were reported to work best by determining their relationship with identified trial outcome measures. In addition to summarizing individual trials, these extracted data elements allow automatic synthesis of results across many trials on the same topic. We apply the system at scale to all reports of randomized controlled trials indexed in MEDLINE, powering the automatic generation of evidence maps, which provide a global view of the efficacy of different interventions combining data from all relevant clinical trials on a topic. We make all code and models freely available alongside a demonstration of the web interface.