ASReview

Active learning for Systematic Reviews

About ASReview

Anyone who goes through the process of screening large amounts of texts such as newspapers, scientific abstracts for a systematic review, or ancient texts, knows how labor intensive this can be. With the rapidly evolving field of Artificial Intelligence (AI), the large amount of manual work can be reduced or even completely replaced by software using active learning.

The Zen of ElasBy using our AI-aided tools, published in Nature Machine Intelligence, you can not only save time, but you can also increase the quality of your screening process. ASReview enables you to screen more texts than the traditional way of screening in the same amount of time. Which means that you can achieve a higher quality than when you would have used the traditional approach.

Consider the example of systematic reviews, which are “top of the bill” in research. However, the number of scientific papers on any topic is skyrocketing. Since it is of crucial importance for the advancement of science to produce high-quality systematic review articles, sometimes as quickly as possible in times of crisis, we need to find a way to effectively automate this screening process. Before Elas* was there to help you, systematic reviewing was an exhaustive task, often very boring.

*Your Electronic Learning Assistant who comes with ASReview, read more about The principles of Elas.

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Systematic Reviewing in the pre-Elas era

Keyword search

Traditionally, the pipeline of a classical systematic review starts with the reviewer doing a keyword search to retrieve all potentially relevant references. See also this video.

Abstract Screening

The reviewer can then start screening the abstracts and titles to assess the potential relevance to their particular research question.

Months later

For an experienced reviewer, it takes between 30 seconds to a couple of minutes to classify a single abstract, which easily results in hundreds of hours spent merely on abstract screening.

Result

After all of the abstracts have been screened by the reviewer, the result is a subset of the initial search containing all potentially relevant references. The reviewer reads all of the full-text versions and writes an awesome paper.

Keyword search

Traditionally, the pipeline of a classical systematic review starts with the reviewer doing a keyword search to retrieve all potentially relevant references. See also this video.

Abstract screening

The reviewer can then start screening the abstracts and titles to assess the potential relevance to his/her particular research question.

Months later

For an experienced reviewer, it takes between 30 seconds to a couple of minutes to classify a single abstract, which easily results in hundreds of hours spent merely on abstract screening.

Result

After all of the abstracts have been screened by the reviewer, the result is a subset of the initial search containing all potentially relevant references. The reviewer reads all of the full-text versions and writes his awesome paper.

Systematic Reviewing with Elas

Keyword search

Similarly, in the research cycle of a systematic review with machine-aided systematic reviewing, the reviewer also starts with a keyword search to retrieve all potentially relevant references, downloads these and imports them into a reference manager.

AI-aided abstract screening

Then, the reviewer selects some relevant target papers. A machine learning model is trained on these papers to predict which reference to present next. Subsequently, the reviewer enters the active learning cycle. See also this video.

Only hours later

Since the abstracts are presented from most to least relevant, the reviewer can stop reviewing after having seen all relevant abstracts. This form of active learning will save hundreds or hours time going through all references.

Reproducible Results

Since we embrace Open Science, all decisions made by the reviewer as well as all the technical information is stored in a log-file, which can (or should) be published alongside the paper.

Keyword search

Similarly, in the research cycle of a systematic review with machine-aided systematic reviewing, the reviewer also starts with a keyword search to retrieve all potentially relevant references, downloads these and imports them into a reference manager.

AI-aided abstract screening

Then, the reviewer selects some relevant target papers. A machine learning model is trained on these papers to predict which reference to present next. Subsequently, the reviewer enters the active learning cycle. See also this video.

Only hours later

Since the abstracts are presented from most to least relevant, the reviewer can stop reviewing after having seen all relevant abstracts. This form of active learning will save hundreds or hours time going through all references.

Reproducible results

Since we embrace Open Science, all decisions made by the reviewer as well as all the technical information is stored in a log-file, which can (or should) be published alongside the paper.

Our services

ASReview Lab

Our team is working hard and we are continuously improving our software, and we are very proud of what we can already offer you.

The source code of ASReview is available open source via GitHub. We call for the community to contribute to our open source project and further improve the code!!
Github
We are strong proponents of Open Science. Therefore, the entire project is Open Code and all papers are Open Access. Moreover, your data is yours and will stay yours if you use our software.
Read moreOur Nature MI paper
The ASReview team developed Research software to explore the future of AI in Systematically Reviewing large amounts of textual data. Install ASReview LAB and start reviewing!
Installation: Quick StartInstuction VideoFAQ
The ASReview team developed an extension for researchers and medical doctors to facilitate the reading of literature on the Coronavirus. The extension makes the CORD-19 dataset available in the ASReview LAB and is daily updated with new Corona-related publications.
Read More

Help us to make COVID-19 research accessible to everyone

Although the software is for free (and Open Source), the development is not… please donate and support the development!

A team of dedicated researchers, engineers, and information specialists are working on the ASReview software. Research and development are very time-consuming. For the nice-to-have features of ASReview, we need your help! You can help not only by developing new features yourself or by improving manuals on Github, but also with a (small or large) financial contribution.

Quick Start

The following documentation will guide you in installing the ASReview software. After installation you can watch our instruction video how to use the software, or read the documentation.

Guide for Windows

Step-by-step installation guide to install the ASReview software on your Windows machine

Guide for MacOS

Step-by-step installation guide to install the ASReview software on your MacOS machine

Quick Tour

Have a look at our quick-tour how to initiate a new project and how to set-up your project. And then the fun begins!

From the Blog

We will keep you updated with our adventures.

Scientific Papers

ASReview is a research project and here we present our publications.

Mail
asreview@uu.nl
Find us
Utrecht University, The Netherlands
Twitter
@RensvdSchoot
Github
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