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Artificial intelligence (AI) is increasingly used in scientific research. However, current AI often lacks reproducibility and transparency, hindering its full integration with the scientific method.

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Area of Science:

  • Scientific research methodology
  • Artificial intelligence in science

Background:

  • The application of artificial intelligence (AI) in scientific research is rapidly expanding.
  • There is a growing need to evaluate the impact and suitability of AI tools within the scientific domain.

Purpose of the Study:

  • To assess the impact of artificial intelligence on scientific research.
  • To identify limitations of current AI in adhering to scientific principles.
  • To propose requirements for AI development that align with the scientific method.

Main Methods:

  • Critical review of AI applications in research.
  • Analysis of AI's alignment with core scientific principles such as reproducibility, transparency, and objectivity.
  • Evaluation of AI's capacity for mechanistic understanding.

Main Results:

  • Current artificial intelligence implementations in science frequently exhibit deficiencies in reproducibility.
  • AI tools often lack transparency, hindering the understanding of their decision-making processes.
  • Objectivity and mechanistic understanding are not consistently achieved with existing AI in research.

Conclusions:

  • To maximize the benefits of AI in science, its development must prioritize compatibility with the scientific method.
  • Future AI for research should be designed to ensure reproducibility, transparency, and objectivity.
  • Enhancing AI's mechanistic understanding is crucial for its reliable application in scientific discovery.