AI Tools for Researchers in Scientific Publishing

Artificial intelligence is changing the publishing industry. It’s speeding up publication, improving data-processing capabilities, and helping reviewers check for ethical issues. Moreover, AI tools can benefit researchers too. Here, we’ll explore MDPI’s AI tools for researchers which are being developed to deal with the issues the scientific publishing community is facing. Whilst doing so, we’ll also introduce you to our AI Team.

MDPI’s AI Team

In the world of scientific research and publishing, AI is being implemented to improve processes, services, and the quality of research. MDPI is committed to being at the forefront of Open Access scientific publishing, so we’re very interested in exploring how we can leverage AI to improve our services.

Over the last year, our AI team has worked on setting up the foundational pipeline that is now allowing us to develop and launch different tools. This has involved collaborating with departments across MDPI and studying the broader scientific community to identify key areas to focus on.

On the one hand, we’re aiming to improve the efficiency of our workforce by reducing the amount of repetitive and tedious tasks. On the other hand, we are also deploying tools to improve the user experience of our authors, reviewers, and readers.

Throughout this article, we’ll introduce you to the team and the work they’re doing.

MDPI’s AI tools for researchers

AI is dramatically shifting scientific publishing. As discussed in our previous article, we explored the range of advantages and disadvantages that have emerged from this change.

The advantages include data management and automating tasks, translation aids, and image generation. The disadvantages, however, include difficulty in detecting generative AI, the race between tools to detect AI and growing AI sophistication, and paper mills. To learn more about these issues, read the full article Artificial Intelligence in Scientific Publishing.

Here, we’ll overview what tools we’ve developed to mitigate these issues and to also enhance the experience for our workforce and users.

How MDPI’s AI Team create tools

We spoke to Diogo Rodrigues, Senior Data Scientist, about how the team produces AI tools for researchers and more:

As part of the AI team, we’re committed to creating user-centric tools for both internal and external users. Our primary objective is to deliver valuable applications that optimize workflows such as publishing research and providing support through features like identifying related works or recommending appropriate journals.

To ensure state-of-the-art performance, our team incorporates cutting-edge technologies and innovations, including large language models. We recognize the elevated standards within the scholarly community and strive to exceed your expectations.

Collaboration is key to our success. So, by working closely with business teams—such as scientific officers and editors—we maintain alignment with current developments while ensuring future offerings add significant value to users. Seamless communication between domain experts guarantees that our internal stakeholders and, ultimately, researchers profit from our applications.

Journal Finder and Article Similarity

The number of articles being published, journals established, and amount of overall content being produced far exceeds what anyone can keep track of. The sheer scale of the output in scientific publishing is staggering.

With this in mind, two tools were developed and made available on our website.

First, Journal Finder looks at the title and abstract of your manuscript and suggests a ranked list of journals you can submit to. Additionally, this tool is also used internally once a manuscript is received to verify that the chosen journal fits the scope of the manuscript.

This tool takes the title and abstract and passes them through a natural language processing (NLP) model that generates a numerical representation of the text that can capture its meaning (this is called an embedding). The embedding is then used to do a similarity search among the papers in each journal to find papers that are similar in scope to the submitted manuscript. It looks at patterns within the language of your work and the content in journals, rather than just keywords.

Furthermore, the tool provides a filter to narrow down the search, based on journals’ costs, Impact Factors, and the database index.

Second, Article Similarity is an advanced recommendation engine that uses a similar approach to suggest similar or related papers based one the one you’re viewing. It’s integrated into our main website in every article page.

This approach can be used for peer reviewer recommendations, topic modelling projects, and others.


EtymlogAI will be used in the formulation of MDPI paper categories. Using an AI algorithm, this tool will be able to select and categorise manuscripts under processing as well as published ones.

Once deployed, using a manuscript’s title and abstract, the tool will provide up to five categories for the manuscript. This is like author keywords, but in a more consistent and standardised manner.

This will give us a powerful tool that will allow our editorial staff to have an overview of published papers and help them to make data-driven decisions. For authors, it will help in identifying similar papers to their own and, for readers, it will help in finding related papers published by MDPI.


Ethicality is an AI tool designed to help with the ethical checks during the manuscript submission process for our journals.

It uses state-of-the-art technology, also integrating the capability to identify potentially AI-generated publications. Thanks to the experience and professionalism of our editors and ethical team, we have been able to gather different examples of potential ethical issues that will be used in our training dataset.

It will be fully integrated into our online submission system, SuSy, and will detect and flag potential ethical issues during the technical pre-check phase for new submissions. This will allow our Assistant Editors to quickly check the potential issues and resolve them. Also, it will add valuable information on the submission level that can be checked at any time whilst processing the manuscript.

Furthermore, it features a reference checking function. This feature will automatically search for and, if present, highlight several potential issues with the references, such as self-citation rates, non-related citations, old references, etc.

AI tools and MDPI

The world of artificial intelligence is very vast and dynamic. MDPI is committed to being at the forefront of Open Access scientific publishing. We believe that AI can provide innovative solutions and enhance the experience of interacting with MDPI material. We are producing AI tools for researchers that reflect the values of our company and, more importantly, benefit the whole scientific community.

Diogo Rodrigues, again, summarises MDPI’s ambition with AI:

Our main ambition? To create intuitive tools that simplify author tasks, empowering them to concentrate on what truly matters: advancing research and fostering a better, more sustainable world. With modern technology and active collaboration with domain experts, we continually commit ourselves to develop solutions that support academics.

If you are interested in submitting research and integrating some of these tools into your workflow, have a look at our full journal list. We have over 430 journals, so we are bound to have something that will interest you.

This article was written in collaboration with Enric Sayas, Business Analyst for the AI Team.