⚠️ State of the art of NLP ⚠️
AI tools for extracting scientific information in 2025
Scientific research now produces an astronomical number of publications every year. Between 2016 and 2022, the number of indexed articles rose from 1.92 million to 2.82 million per year, and some estimates are as high as 4.5 million if extended sources are included. This is equivalent to the publication of a new article every 10 seconds or so – a pace that no-one can keep up with manually. This explosion in the volume of information is creating a real information overload for researchers.
Extracting relevant information from this critical mass has therefore become a major challenge. Researchers risk missing out on important work, or wasting precious time sorting through redundant results. Fortunately, the rise of
Artificial intelligence offers new tools to help us cope with this avalanche of data. Sophisticated algorithms can now filter, summarise and prioritise scientific literature on a scale that is impossible to achieve manually. In this article, we’ll look at how AI is transforming scientific intelligence in 2025, what the best tools available are, and how best to use them.
Contents :
1. Current challenges in bibliographic research
2. What AI will change in 2025
3. Overview of the best AI tools
4. Case studies
5. Criteria for choosing the right tool
1. The current challenges of bibliographic research
Before the advent of AI tools, carrying out exhaustive scientific monitoring was a daunting task. Among the most common challenges :
Considerable time
Researchers spend hours searching for articles, sorting results and reading publications to extract the relevant information.
With several thousand new articles published every day in the biomedical field alone, it is physically impossible to cover everything.
Limitations of traditional tools
Platforms such as Google Scholar or PubMed offer raw access to the literature, but their functionalities remain limited.
Keyword searches return results that have to be filtered manually, and these tools offer neither automated summaries nor assessments of the quality of the studies.
Risk of bias and omissions
Manual monitoring is not infallible. Relevant studies can slip under the radar, leading to selection bias. Conversely, we can also download the same information several times (duplication) or rely on unreliable sources.
In short, the traditional method of bibliographic research is manual, time-consuming and largely based on personal intuition. They are showing their limits in the digital age and in the age of scientific Big Data. It is in this context that AI tools have begun to emerge to relieve researchers of the most tedious tasks.
2. What AI will change in 2025
In 2025, artificial intelligence will bring tangible improvements at every stage of the search for scientific information. Here are a few concrete changes:
Automatic extraction of key data
Text-mining AIs can detect the most important elements (methods, main results, conclusions) in an article and extract them automatically. This makes it possible to grasp the essence of a publication without having to read it in its entirety.
For example, they can spot a key figure in a paragraph and highlight it, or find the description of a specific method buried in a 10-page article.
AI-assisted summaries and syntheses
Thanks to advanced language models (including recent GPTs), you can generate a clear summary of a scientific article in a matter of seconds.
Example: Tools such as Opscidia offer instant summaries of complex studies, where it would have taken a human hours to dissect everything. Better still, some assistants can compare several articles and provide a comparative summary.
Intelligent classification of sources
Rather than relying solely on chronological order or the number of citations, AI algorithms can rank search results according to their contextual relevance.
For example, if there are 500 articles on a subject, AI can put at the top of the list those that best correspond to the precise question you are asking, or those that are the consensus in the field. There are also tools for assessing the quality of studies (sample size, prestige of the journal, independent validation, etc.).
Detecting trends and weak signals
By analysing thousands of publications,AI can highlight emerging themes or subtle correlations that would be difficult for the human eye to spot.
For example, it can identify that a new process or molecule is beginning to attract interest in a sub-field, even before it becomes obvious. Intelligent intelligence platforms such as opscidia allow you to set up automated alerts: as soon as a new advance is published on a specific subject, you are informed.
In short, AI acts as an ultra-fast, ubiquitous search assistant. It does not tire when faced with hundreds of PDFs, makes no errors of attention, and can absorb colossal volumes of information to extract meaning. In 2025, these capabilities will mean huge savings in time and quality for researchers who know how to take advantage of them, while giving them ultimate control over the critical analysis of content.
3. Overview of the best AI tools for extracting scientific information
Numerous AI tools have emerged to help researchers filter and exploit the literature. Here is an overview of some of the leading solutions, with their strengths and use cases:
AI platforms that extract sourced information from scientific documents
Opscidia is an innovative French platform that offers an AI-powered scientific search engine that uses augmented retrieval (AR) to extract key passages directly from articles found, quoting their sources precisely.
The result : extract all the paragraphs of a complete text to answer a question and obtain a tailor-made summary, built from the best publications, selected by an AI search engine.
Pioneering AI-based academic research assistants
Iris.ai offers a semanticsearch engine, automatically generates article summaries and extracts specific data.
The result : extract all the formulas or numerical results from a batch of publications for comparison.
A free academic search engine
Semantic Scholar indexes over 200 million articles. When you carry out a search, the algorithm analyses the semantics of your query to find not only articles containing your keywords, but also those dealing with the same concept.
The result : you can decide very quickly whether the article is worth opening. Semantic Scholar also highlights ‘influential’ studies (highly cited by others) for each query, which helps to identify major works.
Assess the reliability of an article and understand the context of its citations
For a given article, Scite indicates how many other subsequent studies have cited it, supporting or contradicting it. At a glance, you can see whether a scientific result is the subject of consensus or dispute.
The result : the AI will formulate a reasoned response based on published studies that it cites as references. It’s like questioning an expert who answers with extracts from publications.
By combining these methods (initial mapping, continuous detection of weak signals, regular comparisons), we structure an exhaustive and dynamic watch. We have a genuine strategic radar covering both the distant evolution of the technology and the immediate movements of the market.
4. Concrete use cases in R&D, teaching or technology watch
How do these tools fit into the day-to-day work of researchers and other science professionals? Here are three scenarios illustrating their contribution:
Context: A doctoral student in biology needs to write the introduction to his thesis, taking stock of the last 10 years of research in his field. Rather than starting from scratch, he uses a combination of Semantic Scholar and Iris.ai. First, he types in a few generic keywords on Semantic Scholar to identify the must-have papers (those that stand out the most and are the most cited). Then, using Iris.ai, he generates summaries of the key articles to quickly check whether they are relevant to his subject. He also asks Iris.ai to extract the "core findings" of certain articles. In one day, he obtains a fairly precise map of the decade's trends and can direct his literature review with full knowledge of the facts.
What AI brings: In this case, AI tools served as a compass for navigating through a bibliographic ocean. The PhD student was able to identify the major works more quickly and filter out the less relevant ones without having to read everything in detail. Of course, he will still have to analyse the selected articles in depth and produce a personal critical synthesis, but the AI has saved him long blind sorting sessions.
Context: An energy start-up is developing a new battery. The R&D team needed to ensure that they had explored all similar approaches published recently in order to refine their technology and avoid reinventing the wheel. They use Opscidia to carry out targeted monitoring: by asking questions such as "new high-density anodes for lithium batteries", they immediately obtain a summary report citing the latest patents and scientific articles on the subject. The tool even tells them that 3 months ago, a Japanese laboratory published promising results on a material close to the one they are considering - information they had not spotted via their manual searches.
What AI brings to the table: In this scenario, AI played the role of technology scout. Opscidia, with its database that also includes patents, was able to detect a potential competitor and adjust the strategy accordingly. The R&D team gains time and visibility over the state of the art, which speeds up innovation and reduces the risk of missing out on a key discovery published elsewhere.
Context: A university lecturer wants to update his course with examples taken from very recent research, to make it more lively and in tune with current scientific developments. He uses Scite.ai to find case studies on a phenomenon he is teaching (for example, the impact of climate change on local biodiversity). The Scite assistant provides him with a summary answer to the question, along with 3 references to articles published in the last year. Digging deeper, the professor noticed that two of these articles had concordant results, while the third offered a different critical point of view. He decided to talk about this in class to show the students that science is also built through debate. He also shares with the students a link to a Semantic Scholar collection that he has put together for them, so that they can read the TLDR summaries of each article before discussing it in class.
The benefits of AI: For teachers, AI tools are a way of enriching their lessons without spending weeks on research. They can identify fresh and relevant examples very quickly, and even use the summaries generated to prepare their explanations. Students, for their part, benefit from easier access to sources and can concentrate on understanding and critical discussion rather than spending hours searching for articles.
5. Criteria for choosing the right scientific AI tool
Given the variety of AI solutions available, how do you select the one that best suits your needs? Here are a few selection criteria to consider:
- Quality and scope of sources: Make sure that the tool covers your field of research and is based on reliable databases. For example, a tool that aggregates the publications of major recognised publishers (Elsevier, Springer, IEEE, PubMed, etc.) will guarantee you a certain quality of content. Conversely, check whether it excludes or flags up potentially dubious sources (predatory conferences, unedited papers, etc.). Some tools also favour access to preprints or patents: this is a plus if you need the most up-to-date information, but bear in mind that a preprint is not peer-reviewed.
- Access to full text: A tool that relies solely on titles and abstracts may lack depth. If possible, choose a solution capable of exploiting the full-text of articles, especially if you are looking for specific information (a figure in the results, a quotation in the discussion). Also, check whether the tool provides easy access to PDFs of articles (via links to arXiv, DOI, Unpaywall, etc.). If it integrates with your university subscription to give you the full text, that’s ideal.
- Summary and sharing features: Depending on your use case, certain features will be invaluable. Do you just need to find references, or do you want the tool to generate summaries for you? Do you want to extract datasets (e.g. all the values of parameter X in the literature)? Do you need to collaborate as a team (shared folder, annotation of references, easy export to a bibliographic manager)? For example, the Iris.ai tool allows you to organise results by project, while Scite.ai offers customised dashboards for tracking subjects. Make sure that the tool you choose fits in well with your workflow and saves you time, from start to finish.
- Security and confidentiality: The question of sensitive data can arise, especially in companies. If you intend to interrogate the AI with confidential information (e.g. unpublished R&D results to obtain comparisons), check the tool’s confidentiality policy. Favour solutions that offer solid guarantees (encrypted data, no re-use of queries for external training purposes, etc.). In some cases, it may make sense to opt for an on-premise version (hosted locally) or an open source solution that you deploy yourself, so that you retain total control over your data.
By evaluating these criteria – and of course the cost and ease of use of the tool – you can choose the AI solution best suited to your context (academic research, competitive intelligence, educational support, etc.). Don’t hesitate to try out several of them: many offer free trials or freemium versions that give you a concrete idea of their advantages.