ASReview: Open Source Software for Efficient and Transparent Active Learning for Systematic Reviews | Semantic Scholar

Abstract:  For many tasks – including guideline development for medical doctors and systematic reviews for research fields – the scientific literature needs to be checked systematically. The current practice is that scholars and practitioners screen thousands of studies by hand to find which studies to include in their review. This is error prone and inefficient. We therefore developed an open source machine learning (ML)-aided pipeline: Active learning for Systematic Reviews (ASReview). We show that by using active learning, ASReview can lead to far more efficient reviewing than manual reviewing, while exhibiting adequate quality. Furthermore, the presented software is fully transparent and open source. 

ASReview: Open Source Software for Efficient and Transparent Active Learning for Systematic Reviews | Semantic Scholar

Abstract:  For many tasks – including guideline development for medical doctors and systematic reviews for research fields – the scientific literature needs to be checked systematically. The current practice is that scholars and practitioners screen thousands of studies by hand to find which studies to include in their review. This is error prone and inefficient. We therefore developed an open source machine learning (ML)-aided pipeline: Active learning for Systematic Reviews (ASReview). We show that by using active learning, ASReview can lead to far more efficient reviewing than manual reviewing, while exhibiting adequate quality. Furthermore, the presented software is fully transparent and open source. 

New MIT Press Journal to Debunk Bad COVID-19 Research

“Preprint servers play an increasingly important role in the scholarly publishing landscape. They are a popular platform for researchers to get early feedback on their research. They are also a space where researchers can publish research products and data sets not typically published in traditional journals. The process is fast — publication of open-access research that anyone can read is immediate.

The downside of this open publication system is that sometimes controversial or poor-quality research can garner a lot of attention on social media or in news articles, said Stefano Bertozzi, professor of health policy and management at the University of California, Berkeley, School of Public Health. In the clamor for information about COVID-19, it is easy for misinformation to spread online, he said.

To combat this, MIT Press and the Berkeley School of Public Health are launching a new COVID-19 journal, one that will peer review preprint articles getting a lot of attention — elevating the good research and debunking the bad.

The Rapid Reviews: COVID-19 journal will be led by Bertozzi, who will serve as the first editor in chief. Unlike a traditional journal, authors will not submit their work for review. Instead, the Rapid Reviews team will select and review already-published preprint articles — a publishing model known as an overlay journal.   …”

The MIT Press and UC Berkeley launch Rapid Reviews: COVID-19 · Rapid Reviews COVID-19

“The MIT Press announced today the launch of Rapid Reviews: COVID-19 (RR:C19), an open access, rapid-review overlay journal that will accelerate peer review of COVID-19-related research and deliver real-time, verified scientific information that policymakers and health leaders can use….

Using artificial intelligence tools, a global team will identify promising scholarship in preprint repositories, commission expert peer reviews, and publish the results on an open access platform in a completely transparent process. The journal will strive for disciplinary and geographic breadth, sourcing manuscripts from all regions and across a wide variety of fields, including medicine; public health; the physical, biological, and chemical sciences; the social sciences; and the humanities. RR:C19 will also provide a new publishing option for revised papers that are positively reviewed….”

AI tool searches thousands of scientific papers to guide researchers to coronavirus insights

“The scientific community worldwide has mobilized with unprecedented speed to tackle the COVID-19 pandemic, and the emerging research output is staggering. Every day, hundreds of scientific papers about COVID-19 come out, in both traditional journals and non-peer-reviewed preprints. There’s already far more than any human could possibly keep up with, and more research is constantly emerging.

And it’s not just new research. We estimate that there are as many as 500,000 papers relevant to COVID-19 that were published before the outbreak, including papers related to the outbreaks of SARS in 2002 and MERS in 2012. Any one of these might contain the key information that leads to effective treatment or a vaccine for COVID-19.

Traditional methods of searching through the research literature just don’t cut it anymore. This is why we and our colleagues at Lawrence Berkeley National Lab are using the latest artificial intelligence techniques to build COVIDScholar, a search engine dedicated to COVID-19. COVIDScholar includes tools that pick up subtle clues like similar drugs or research methodologies to recommend relevant research to scientists. AI can’t replace scientists, but it can help them gain new insights from more papers than they could read in a lifetime….”

Exploring the COVID-19 network of scientific research with SciSight | AI2 Blog

“Three months into the coronavirus pandemic, the world’s scientific knowledge of the SARS-CoV-2 virus is rapidly expanding. Reports of potential vaccines and treatments sprout up almost daily. Thousands of papers have been pouring into Semantic Scholar’s COVID-19 Open Research Dataset (CORD-19), a collection of nearly 60,000 scientific publications of potential relevance to the topic, both historical and cutting-edge….

To help accelerate scientific discovery with visualization, last month we launched SciSight, a framework of exploratory search and visualization tools for the COVID-19 literature. The first version of SciSight supported exploring associations between biomedical concepts appearing in the literature. In preliminary user interviews, the tool was found helpful in discovery-oriented search. We now release two important updates of SciSight….”

Exploring the COVID-19 network of scientific research with SciSight | AI2 Blog

“Three months into the coronavirus pandemic, the world’s scientific knowledge of the SARS-CoV-2 virus is rapidly expanding. Reports of potential vaccines and treatments sprout up almost daily. Thousands of papers have been pouring into Semantic Scholar’s COVID-19 Open Research Dataset (CORD-19), a collection of nearly 60,000 scientific publications of potential relevance to the topic, both historical and cutting-edge….

To help accelerate scientific discovery with visualization, last month we launched SciSight, a framework of exploratory search and visualization tools for the COVID-19 literature. The first version of SciSight supported exploring associations between biomedical concepts appearing in the literature. In preliminary user interviews, the tool was found helpful in discovery-oriented search. We now release two important updates of SciSight….”

Google AI Blog: An NLU-Powered Tool to Explore COVID-19 Scientific Literature

“Due to the COVID-19 pandemic, scientists and researchers around the world are publishing an immense amount of new research in order to understand and combat the disease. While the volume of research is very encouraging, it can be difficult for scientists and researchers to keep up with the rapid pace of new publications. Traditional search engines can be excellent resources for finding real-time information on general COVID-19 questions like “How many COVID-19 cases are there in the United States?”, but can struggle with understanding the meaning behind research-driven queries. Furthermore, searching through the existing corpus of COVID-19 scientific literature with traditional keyword-based approaches can make it difficult to pinpoint relevant evidence for complex queries.

To help address this problem, we are launching the COVID-19 Research Explorer, a semantic search interface on top of the COVID-19 Open Research Dataset (CORD-19), which includes more than 50,000 journal articles and preprints. We have designed the tool with the goal of helping scientists and researchers efficiently pore through articles for answers or evidence to COVID-19-related questions….”

Google’s new AI-powered search tool helps researchers with coronavirus queries

“Google‘s AI team has released a new tool to help researchers traverse through a trove of coronavirus papers, journals, and articles. The COVID-19 research explorer tool is a semantic search interface that sits on top of the COVID-19 Open Research Dataset (CORD-19). …”