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Multiple file formats
Easily import and manage different file types without restrictions.
Circular data flow
Re-import exports into reference software for a circular workflow.
Reproducible
All steps are retractable complying to the highest level of compliance.
From data to action
Easily prepare your project and stay compatible from the start.
RIS in = RIS out
Smarter by default
Built for simplicity, with the depth to explore model impact when you’re ready.
Reproduce, support, and advise
Ensure transparency and support your researchers with trusted examples and reporting tools.
“ASReview is the scientifically sound answer to handling the growing number of scientific references, found after literature search.”
– Tale Evenhuis
Courses, coffee and collaborations
Dive into online learning materials or join us in person for discussion and inspiration.
Education for professionals
A variation of expert-led courses that will equip you with the essential knowledge and skills to oversee AI-assisted screening projects in your organization granting national and international credits.
On-demand training
An in-house training tailored to ensure all team members are onboarded efficiently and equipped to use ASReview effectively. Build consistency across your team and integrate into your processes with ease.
Integrated and cited by multiple researchers and fields
Browse a growing collection of publications using ASReview as useful examples to support your researchers.
Abstract
It is of utmost importance to provide an overview and strength of evidence of predictive factors and to investigate the current state of affairs on evidence for all published and hypothesized factors that contribute to the onset, relapse, and maintenance of anxiety-, substance use-, and depressive disorders. Thousands of such articles have been published on potential factors of CMDs, yet a clear overview of all preceding factors and interaction between factors is missing. Therefore, the main aim of the current project was to create a database with potentially relevant papers obtained via a systematic. The current paper describes every step of the process of constructing the database, from search query to database. After a broad search and cleaning of the data, we used active learning using a shallow classifier and labeled the first set of papers. Then, we applied a second screening phase in which we switched to a different active learning model (i.e., a neural net) to identify difficult-to-find papers due to concept ambiguity. In the third round of screening, we checked for incorrectly included/excluded papers in a quality assessment procedure resulting in the final database. All scripts, data files, and output files of the software are available via Zenodo (for Github code), the Open Science Framework (for protocols, output), and DANS (for the datasets) and are referred to in the specific sections, thereby making the project fully reproducible.
Abstract
Objectives
In a time of exponential growth of new evidence supporting clinical decision-making, combined with a labor-intensive process of selecting this evidence, methods are needed to speed up current processes to keep medical guidelines up-to-date. This study evaluated the performance and feasibility of active learning to support the selection of relevant publications within medical guideline development and to study the role of noisy labels.
Design
We used a mixed-methods design. Two independent clinicians’ manual process of literature selection was evaluated for 14 searches. This was followed by a series of simulations investigating the performance of random reading versus using screening prioritization based on active learning. We identified hard-to-find papers and checked the labels in a reflective dialogue.
Main outcome measures
Inter-rater reliability was assessed using Cohen’s Kappa (ĸ). To evaluate the performance of active learning, we used the Work Saved over Sampling at 95% recall (WSS@95) and percentage Relevant Records Found at reading only 10% of the total number of records (RRF@10). We used the average time to discovery (ATD) to detect records with potentially noisy labels. Finally, the accuracy of labeling was discussed in a reflective dialogue with guideline developers.
Results
Mean ĸ for manual title-abstract selection by clinicians was 0.50 and varied between − 0.01 and 0.87 based on 5.021 abstracts. WSS@95 ranged from 50.15% (SD = 17.7) based on selection by clinicians to 69.24% (SD = 11.5) based on the selection by research methodologist up to 75.76% (SD = 12.2) based on the final full-text inclusion. A similar pattern was seen for RRF@10, ranging from 48.31% (SD = 23.3) to 62.8% (SD = 21.20) and 65.58% (SD = 23.25). The performance of active learning deteriorates with higher noise. Compared with the final full-text selection, the selection made by clinicians or research methodologists deteriorated WSS@95 by 25.61% and 6.25%, respectively.
Conclusion
While active machine learning tools can accelerate the process of literature screening within guideline development, they can only work as well as the input given by human raters. Noisy labels make noisy machine learning.
Abstract
Governments use nudges to stimulate citizens to exercise, save money and eat healthily. However, nudging is controversial. How the media frames nudge impacts decisions on whether to use this policy instrument. We, therefore, analyzed 443 newspaper articles about nudging. Overall, the media was positive about nudges. Nudging was viewed as an effective and efficient way to change behavior and received considerable support across the political spectrum. The media also noted that nudges were easy to implement. The controversy about nudges concerns themes like paternalism, fear of manipulation, small effect sizes, and unintended consequences. Academic proponents of nudging were actively involved in media debates, while critical voices were less often heard. There were some reports criticizing how the government used nudges. However, these were exceptions; the media often highlighted the benefits of nudging. Concluding, we show how nudging by governments was discussed in a critical institution: the news media.
Abstract
Context
Predictive maintenance is a technique for creating a more sustainable, safe, and profitable industry. One of the key challenges for creating predictive maintenance systems is the lack of failure data, as the machine is frequently repaired before failure. Digital Twins provide a real-time representation of the physical machine and generate data, such as asset degradation, which the predictive maintenance algorithm can use. Since 2018, scientific literature on the utilization of Digital Twins for predictive maintenance has accelerated, indicating the need for a thorough review.
Objective
This research aims to gather and synthesize the studies that focus on predictive maintenance using Digital Twins to pave the way for further research.
Method
A systematic literature review (SLR) using an active learning tool is conducted on published primary studies on predictive maintenance using Digital Twins, in which 42 primary studies have been analyzed.
Results
This SLR identifies several aspects of predictive maintenance using Digital Twins, including the objectives, application domains, Digital Twin platforms, Digital Twin representation types, approaches, abstraction levels, design patterns, communication protocols, twinning parameters, and challenges and solution directions. These results contribute to a Software Engineering approach for developing predictive maintenance using Digital Twins in academics and the industry.
Conclusion
This study is the first SLR in predictive maintenance using Digital Twins. We answer key questions for designing a successful predictive maintenance model leveraging Digital Twins. We found that to this day, computational burden, data variety, and complexity of models, assets, or components are the key challenges in designing these models.
Abstract
Risk assessment of chemicals is a time-consuming process and needs to be optimized to ensure all chemicals are timely evaluated and regulated. This transition could be stimulated by valuable applications of in silico Artificial Intelligence (AI)/Machine Learning (ML) models. However, implementation of AI/ML models in risk assessment is lagging behind. Most AI/ML models are considered ‘black boxes’ that lack mechanistical explainability, causing risk assessors to have insufficient trust in their predictions.
Here, we explore ‘trust’ as an essential factor towards regulatory acceptance of AI/ML models. We provide an overview of the elements of trust, including technical and beyond-technical aspects, and highlight elements that are considered most important to build trust by risk assessors. The results provide recommendations for risk assessors and computational modelers for future development of AI/ML models, including: 1) Keep models simple and interpretable; 2) Offer transparency in the data and data curation; 3) Clearly define and communicate the scope/intended purpose; 4) Define adoption criteria; 5) Make models accessible and user-friendly; 6) Demonstrate the added value in practical settings; and 7) Engage in interdisciplinary settings. These recommendations should ideally be acknowledged in future developments to stimulate trust and acceptance of AI/ML models for regulatory purposes.
Multiple ways of integration
Integrate in the way that best fits your setup and needs.
Questions & Answers
What security measures are in place to ensure data protection?
By default, ASReview runs locally on your own computer, so your data never leaves your environment. You remain in full control of who accesses your project. If you host ASReview on a server, the data never leaves your environment and is protected according to your organization’s policies.
What is the pricing structure?
ASReview is completely free and open source, supported by a global community of researchers and institutions. There are no licensing fees, subscriptions, or hidden costs—only any potential costs associated with running your own hardware or server infrastructure. Feel free to contribute.
How are software updates and ongoing maintenance managed?
We release updates regularly through official open-source channels (i.e., GitHub and PyPI). You can install or upgrade at your own pace, ensuring the latest features and bug fixes. Because it’s community-driven, anyone can contribute enhancements or report issues, making maintenance a shared effort.
How can it be free to use?
ASReview is an open-source project firmly rooted in academic research, developed by a global community of researchers and institutions. We believe that making systematic reviews more efficient and transparent will accelerate scientific discovery, which is why there are no subscription fees or hidden costs. All you need is a computer with Python installed or a server-based solution provided by your organization. While it’s free in a monetary sense, many contributors devote their own time and resources—including research grants—to continuously improve the software.
Why isn’t it just a website with a login?
We prioritize user control, privacy, and transparency. By letting you install ASReview locally or on your own server, your data stays with you and you are in control. This approach aligns with the open-source philosophy of giving users full ownership of their software and data.
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