# Open Source Software for Efficient and Transparent Reviews

arXiv: https://arxiv.org/abs/2006.12166 · HTML: https://mlsanity.com/paper/2006.12166/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2020.

## Built on by (most cited first, 30 of 35)

- [Explainable artificial intelligence (XAI) in deep learning-based medical image analysis](https://mlsanity.com/paper/2107.10912/) (2021)
- [Artificial Intelligence for Literature Reviews: Opportunities and Challenges](https://mlsanity.com/paper/2402.08565/) (2024)
- [Automated Paper Screening for Clinical Reviews Using Large Language Models](https://mlsanity.com/paper/2305.00844/) (2023)
- [Clinfo.ai: An Open-Source Retrieval-Augmented Large Language Model System for Answering Medical Questions using Scientific Literature](https://mlsanity.com/paper/2310.16146/) (2023)
- [The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence](https://mlsanity.com/paper/2408.12622/) (2024)
- [Explainability in Practice: A Survey of Explainable NLP Across Various Domains](https://mlsanity.com/paper/2502.00837/) (2025)
- [Assessing the Ability of ChatGPT to Screen Articles for Systematic Reviews](https://mlsanity.com/paper/2307.06464/) (2023)
- [Overcoming Data Scarcity in Generative Language Modelling for Low-Resource Languages: A Systematic Review](https://mlsanity.com/paper/2505.04531/) (2025)
- [Streamlining Systematic Reviews: A Novel Application of Large Language Models](https://mlsanity.com/paper/2412.15247/) (2024)
- [AISysRev -- LLM-based Tool for Title-abstract Screening](https://mlsanity.com/paper/2510.06708/) (2025)
- [The Algorithmic State Architecture (ASA): An Integrated Framework for AI-Enabled Government](https://mlsanity.com/paper/2503.08725/) (2025)
- [Evaluating Prompting Strategies and Large Language Models in Systematic Literature Review Screening: Relevance and Task-Stage Classification](https://mlsanity.com/paper/2510.16091/) (2025)
- [PSEUDo: Interactive Pattern Search in Multivariate Time Series with Locality-Sensitive Hashing and Relevance Feedback](https://mlsanity.com/paper/2104.14962/) (2021)
- [Building Trustworthy AI for Materials Discovery: From Autonomous Laboratories to Z-scores](https://mlsanity.com/paper/2512.01080/) (2025)
- [PeroMAS: A Multi-agent System of Perovskite Material Discovery](https://mlsanity.com/paper/2602.13312/) (2026)
- [CRUISE-Screening: Living Literature Reviews Toolbox](https://mlsanity.com/paper/2309.01684/) (2023)
- [Why can't Epidemiology be automated (yet)?](https://mlsanity.com/paper/2507.15617/) (2025)
- [Automatic selection of primary studies in systematic reviews with evolutionary rule-based classification](https://mlsanity.com/paper/2509.23981/) (2025)
- [Computational Measurement of Political Positions: A Review of Text-Based Ideal Point Estimation Algorithms](https://mlsanity.com/paper/2511.13238/) (2025)
- [OpenExtract: Automated Data Extraction for Systematic Reviews in Health](https://mlsanity.com/paper/2603.13338/) (2026)
- [Trends in AI and Human-AI Interaction in Clinical Trials -- A Hybrid Human-AI Exploration](https://mlsanity.com/paper/2605.29096/) (2026)
- [Regularized Data Programming with Automated Bayesian Prior Selection](https://mlsanity.com/paper/2210.08677/) (2022)
- [FairPilot: An Explorative System for Hyperparameter Tuning through the Lens of Fairness](https://mlsanity.com/paper/2304.04679/) (2023)
- [Performance of models for monitoring sustainable development goals from remote sensing: A three-level meta-regression](https://mlsanity.com/paper/2601.06178/) (2026)
- [From Emotion to Expression: Theoretical Foundations and Resources for Fear Speech](https://mlsanity.com/paper/2601.17132/) (2026)
- [From Toil to Thought: Designing for Strategic Exploration and Responsible AI in Systematic Literature Reviews](https://mlsanity.com/paper/2603.05514/) (2026)
- [TiAb Review Plugin: A Browser-Based Tool for AI-Assisted Study Selection in Systematic Reviews](https://mlsanity.com/paper/2604.08602/) (2026)
- [Generative Artificial Intelligence for Literature Reviews](https://mlsanity.com/paper/2605.16475/) (2026)
- [MetaSyn: A Benchmark for LLM Agents on Meta-Analysis Articles from Nature Portfolio](https://mlsanity.com/paper/2606.17041/) (2026)
- [Explainable AI for the EU Right to Explanation: A Systematic Review of the Law-XAI Translation Gap](https://mlsanity.com/paper/2608.02699/) (2026)

## Abstract (arXiv)

To help researchers conduct a systematic review or meta-analysis as efficiently and transparently as possible, we designed a tool (ASReview) to accelerate the step of screening titles and abstracts. For many tasks - including but not limited to systematic reviews and meta-analyses - the scientific literature needs to be checked systematically. Currently, scholars and practitioners screen thousands of studies by hand to determine which studies to include in their review or meta-analysis. This is error prone and inefficient because of extremely imbalanced data: only a fraction of the screened studies is relevant. The future of systematic reviewing will be an interaction with machine learning algorithms to deal with the enormous increase of available text. We therefore developed an open source machine learning-aided pipeline applying active learning: ASReview. We demonstrate by means of simulation studies that ASReview can yield far more efficient reviewing than manual reviewing, while providing high quality. Furthermore, we describe the options of the free and open source research software and present the results from user experience tests. We invite the community to contribute to open source projects such as our own that provide measurable and reproducible improvements over current practice.