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Why Specialised AI has become Essential in R&D

IA
R&D

5 min 

In the world of Research & Development, innovation is the engine of growth. However, a paradox is currently holding back even the most brilliant teams: in order to innovate, you need to know what already exists, but the volume of existing information has become humanly unmanageable.

With approximately 100,000 scientific papers published each year in each specific field (and many more in oncology and AI), manually compiling an exhaustive overview of the current state of the art is nothing short of utopian. As Sylvain Massip pointed out during the recent webinar on monitoring technologies: “To read 100,000 papers in a year, you would need a team of 45 full-time staff.”

Faced with this reality, traditional methodologies (Excel + PubMed + Google Scholar) are becoming obsolete. A new era is dawning: that of human-AI collaboration, where artificial intelligence does not replace researchers, but acts as a cognitive exoskeleton.

Summary:

  1. Focusing the topic: From vague brainstorming to structured exploration
  2. Sourcing and Screening: Intelligent Aggregation
  3. The solution: Unified data warehouse and graphical visualisation
  4. Qualitative Analysis: The end of “Ctrl+F5”.
  5. Why ChatGPT is not sufficient for science
  6. Conclusion: The future is collaborative.
 
 
 
 

1. Framing the topic: From vague brainstorming to structured exploration

The traditional method

 
 
 
 

Getting started with a new state of the art is often laborious. You have to define keywords, try risky Boolean combinations, and hope you don’t miss a crucial synonym. This “framing” stage usually takes 1 to 2 hours and relies entirely on the researcher’s intuition.

 
 
 
 
 

The AI-augmented approach (Agent Search)

 
 
 
 
 

L’IA permet désormais d’inverser le processus. Au lieu de chercher des mots-clés, le chercheur pose une question en langage naturel (ex: “Quels sont les verrous technologiques actuels dans les thérapies géniques ?”).

L’IA, via des agents de recherche, va :

  1. Optimiser les requêtes elle-même.
  2. Identifier des sous-sujets (clusters) pertinents.
  3. Proposer un résumé préliminaire pour chaque axe.
 
 
 
 

The benefit: You no longer start with a blank page. AI suggests angles of attack that the researcher may not have considered, transforming a passive research task into an active validation task.

 
 
 
 
 
 
 

2. Sourcing and Screening: Intelligent Aggregation

This is where the technological divide is most visible. Database fragmentation is a watcher’s nightmare.

The challenge of “multi-bases”

A researcher must juggle between:

  • PubMed for biomedical research.
  • Google Scholar for grey literature.
  • ClinicalTrials for ongoing trials.
  • Espacenet or WIPO for patents.

This step requires manually deduplicating the results in an Excel file, a time-consuming task (5 to 10 hours) with no added value.

 
 
 
 

3. The solution: Unified data warehouse and graphical visualisation

Next-generation platforms such as Opscidia centralise these flows (articles, patents, theses, European projects) in a single repository containing over 200 million documents.

But the real revolution lies in sorting. Instead of a linear list of 15,000 results, AI enables dynamic visualisation along two critical axes:

  • X-axis: Relevance (Semantic proximity to the query).
  • Y-axis: Impact (Research quality, citations, impact factor).

Conceptual Diagram: Filtering by Graph

 
 
 
 

This visualisation allows researchers to visually define their own quality threshold, instantly isolating the “gems” from the incidental documents.

 
 
 
 

4. Qualitative Analysis: The End of "Ctrl+F"

Once 50 documents have been selected, they must be read. This is the absolute bottleneck.

  • Manual method: Open each PDF, search for keywords (Ctrl+F), copy and paste fragments into Word. High risk of error and cognitive fatigue.
  • AI method (Chat with Corpus): Researchers can now “chat” with their selection of articles.

Example of interaction:

 
 
 
 

Key point to note: Unlike generic tools such as ChatGPT or Perplexity, which can hallucinate, AI systems specialising in science display sources sentence by sentence (Source: Article X, Paragraph Y). Users can verify the information with a single click.

 
 
 
 
 
 

5. Synthesis and Writing: AI as an "Expert Intern"

Writing is often the most dreaded stage (writer’s block). The modern approach is based on the concept: “AI proposes, the expert disposes”.

The ideal workflow breaks down as follows:

 
 
 
 
 
 

Comparison Table: Time invested for a complete State of the Art

Stage

Manual Method (Est.)

AI-assisted method (Est.)

Framing

2h

0.5h

Search & Sort

5h

1h

Reading & Analysis

1 day +

2h

Writing

1 day +

2h

TOTAL

~3 to 4 days

~1 day

 
 

Result: An observed time saving of 50 to 60% across the entire process, allowing researchers to focus on critical analysis rather than data collection.

 
 
 
 
 
 

6. Pourquoi "ChatGPT" ne suffit pas pour la science

One recurring question deserves to be addressed: Why pay for a specialised platform when ChatGPT or Perplexity exist?

The answer can be summarised in three key points, which were discussed during the webinar:

  • Comprehensiveness vs Random Selection: Perplexity will often select 3 or 4 web articles to respond. A dedicated platform scans millions of documents to provide a statistical and comprehensive view.
  • Data Security (Sovereignty): For an R&D company, uploading its research areas to an American server (OpenAI) poses a risk to intellectual property. Professional solutions (such as Opscidia) often use sovereign clouds (e.g. Scaleway in France) and do not retrain their models on your data.

Hallucination Management: In science, false references are unacceptable. Specialised RAG (Retrieval-Augmented Generation) pipelines constrain AI to respond only on the basis of the documents provided, drastically reducing the error rate.

 
 
 
 
 
 

Conclusion: The future is collaborative

Artificial intelligence will not replace scientists. However, scientists who use AI will replace those who do not.

The adoption of these platforms is transforming scientific monitoring, transforming it from a “necessary chore” into a fast and accurate strategic lever. By freeing up 60% of their time, researchers can finally devote themselves to what no machine can do: interpret, imagine and innovate.