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<\/figure>\n<\/div><\/div>\n\n\n\nLooking to learn more about AI?<\/strong><\/p>\n\n\n\n Explore our AI Focus<\/strong> section<\/p>\n<\/div>\n<\/div><\/section>\n\n\n\n Our articles are part of EFFISCIANCE<\/mark><\/strong>, our AI enablement offering dedicated to the use of AI in science<\/mark><\/strong>.<\/p>\n\n\n\n Discover all our articles<\/a><\/strong><\/mark><\/p>\n<\/div>\n<\/div><\/section>\n<\/div>\n<\/div><\/section>\n<\/div>\n<\/div><\/section>\n\n\n\n The term \u201cAI\u201d covers a broad range of technologies with very different capabilities. In a scientific context, it is useful to distinguish between two major families: analytical AI<\/strong> and generative AI<\/strong>.<\/p>\n\n\n\n Analytical AI<\/strong> is used to process existing data in order to classify and interpret it, identify key information, predict behavior, or optimize decisions. It can support tasks such as modeling, pattern recognition, segmentation, decision support, and certain types of statistical analyses. Its primary focus is analysis and prediction.<\/p>\n\n\n\n Examples:<\/p>\n\n\n\n Good to know:<\/strong><\/p>\n\n\n\n Predictive AI<\/strong> is a branch of analytical AI<\/strong>. It is distinguished by its ability to anticipate an outcome that has not yet been observed<\/strong>, based on existing data. In other words, all predictive AI is analytical, but not all analytical AI is necessarily predictive.<\/p>\n<\/div>\n<\/div><\/section>\n<\/div>\n<\/div><\/section>\n\n\n\n Generative AI<\/strong>, on the other hand, produces new content, including text, code, images, and summaries. Unlike analytical AI, it does not merely extract information. Instead, it generates a likely output based on patterns learned from very large training datasets. This probabilistic nature is precisely what makes generative AI so useful in everyday work, but also creates vulnerabilities when it comes to scientific accuracy.<\/p>\n\n\n\n Examples:<\/p>\n\n\n\n In a scientific context, the distinction can be summarized as follows:<\/p>\n\n\n\n Although generative AI can never replace a researcher\u2019s expertise, it excels at accelerating the most time-consuming tasks. Here is how it can be successfully integrated into each stage of a research project.<\/p>\n\n\n\n At the outset of a project, AI can help explore a topic quickly, uncover new perspectives, reframe a research question, or suggest working hypotheses. When used effectively, it becomes a powerful tool for generating ideas<\/strong> and clarifying thinking<\/strong>.<\/p>\n\n\n\n Its value, however, depends on the researcher\u2019s critical judgment. A convincing formulation generated by AI is not necessarily a well-founded hypothesis. AI can help broaden the discussion, but it should not be used to bring the analysis to a premature conclusion.<\/p>\n\n\n\n AI is particularly useful for conducting an initial review of the literature. It can help researchers analyze a body of publications, group topics, and extract key concepts. In this way, it acts as a valuable filter and synthesis assistant, making it easier to stay current with developments in a field.<\/p>\n\n\n\n This use nevertheless requires caution. Bibliographic hallucinations, inaccurate or fabricated citations, and gaps or biases in the literature covered are major limitations. An answer may be fluent and clearly written while relying on articles or authors that have been entirely fabricated.<\/p>\n\n\n\n Best practice is therefore to limit AI to summarizing, organizing, or navigating a corpus of documents that you provide. This is known as a Retrieval-Augmented Generation, or RAG, approach. AI should not simply be left to conduct the search on its own.<\/p>\n\n\n\n During the analysis phase, AI can be an invaluable assistant for processing data. Whether structuring a complex dataset, identifying broad trends, or automating time-consuming operations, it can save researchers a considerable amount of time.<\/p>\n\n\n\n When analysis involves programming, AI can also provide valuable support. It can explain how a function works, generate a code skeleton in Python or R, suggest a query, or troubleshoot a bug. For scientists whose primary expertise is not coding, it can significantly reduce technical friction.<\/p>\n\n\n\n However, an AI-generated analysis or piece of code may appear perfectly logical while applying an inappropriate statistical method. These unnoticed processing errors are one of the greatest risks. AI can accelerate execution, but it still requires testing, expert review, and explicit documentation of every step.<\/p>\n\n\n\n AI can also help structure a research paper, rephrase a passage, simplify an explanation, translate a text, or adapt content for different audiences. It is particularly useful for improving clarity and repurposing content for different editorial formats.<\/p>\n\n\n\n Here again, everything depends on the scope of the task assigned to it. When used strictly as a writing aid, AI can save a considerable amount of time. But if it is asked to generate content from scratch without supervision or verification, the result may be unusable: standardized and impersonal writing, a loss of scientific precision, and a risk of hallucinations.<\/p>\n\n\n\n These topics are central to our EFFISCIANCE offering<\/mark><\/a><\/strong>, dedicated to integrating AI into scientific research, and are regularly explored during our webinars<\/mark><\/a><\/strong>.<\/p>\n\n\n\n Watch one of our EFFISCIANCE trainers present a complete use case: How to combine scientific literature monitoring and writing with AI, from exploring the literature with Consensus<\/strong> to working with that material in Claude<\/strong>?<\/em><\/em><\/p>\n\n\n\n Scientific research: from literature monitoring to AI-assisted writing<\/strong><\/p>\n\n\n\nAI in scientific research: what are we actually talking about?<\/mark><\/strong><\/h2>\n\n\n\n
Analytical AI and generative AI: two different approaches<\/mark><\/strong><\/h3>\n\n\n\n
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Generative AI: its real value across the research lifecycle<\/mark><\/strong><\/h2>\n\n\n\n
Exploring a topic and formulating hypotheses<\/mark><\/strong><\/h3>\n\n\n\n
Literature reviews and scientific monitoring<\/mark><\/strong><\/h3>\n\n\n\n
Analysis, coding and data processing<\/mark><\/strong><\/h3>\n\n\n\n
Scientific writing and communication<\/mark><\/strong><\/h3>\n\n\n\n
VIDEO USE CASE: AI IN ACTION<\/h3>\n\n\n\n
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