For half a century, scientific research has produced millions of studies. Too often, they accumulate without being reread, verified or replicated. Artificial intelligence now offers an unprecedented solution: reread, cross and audit the entire scientific corpus to identify biases, dead ends and erroneous hypotheses.
For twenty years, the reproducibility crisis has haunting science. In 2012, Amgen revealed that he had been able to reproduce only 6 of the 53 major studies which she had selected in oncology. In psychology, more than 60 % of experiences do not dose in the face of an independent verification. In many areas, the publication dynamics have taken precedence over the verification requirement.
The problem is structural. Economic incentives, the pressure of journals, the funding oriented towards “subjects with results” generated a massive accumulation of sometimes dubious data. Certain central hypotheses, such as that of the beta-amyloid protein in Alzheimer, have oriented for decades billions of euros in research, without final validation.
The global audit becomes possible
Large language models (LLM) change the scale of the possible. Capable of ingesting thousands of scientific articles, they can:
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- cross papers that do not cite each other;
- detect low correlations or statistical errors;
- Identify the areas neglected by research;
- Evaluate the robustness of a corpus without cognitive bias.
The result: a global, exhaustive, neutral rereading. In a few hours, a model can identify inconsistencies that isolated researchers would take years to spot. The audit becomes automatic, systematic.
Towards a search controlled by AI
Beyond the analysis, some laboratories are starting to integrate AI into the scientific creation process itself. Platforms such as benching or Emerald Cloud Lab make it possible to transform the hypotheses generated by AI into concrete experiences, carried out in automated laboratories.
The sequence becomes mechanical: the AI reads literature, identifies a flaw or a lack, offers a protocol, sends it to a robot, recovers the results, analysis, and start again. Research becomes continuous, without break, without fatigue, without intuition, but with rigor.
The end of the error … or discovery?
There resides the limit. An essential part of research is due to the error, at random, to the accident. Penicillin, microwave or viagra were born from unforeseen deviations. AI, designed to reduce deviations, could dry up this source of innovation.
Some teams already explore mechanisms to artificially introduce “noise” into the models. Objective: simulate serendipity, cause the constructive accident. But can we program the unexpected?
Short -term concrete effects
The impact will be considerable in many sectors:
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- In pharmacies, abandoned molecules may be rediscovered or repositioned.
- In medicine, personal health data crossed with the literature will identify targeted treatments.
- In public laboratories, scientific productivity will be multiplied, subject to controlled access to tools.
Owner databases will become strategic. In this new diet, the model has less than the data it can explore. Hospitals, states, universities will become holders of knowing how to revalue.
