Artificial Intelligence in Analytical Chemistry: Towards Yet Undiscovered Opportunities

TL;DR

The paper explores how AI can transform analytical chemistry beyond current uses, suggesting new ways to enhance scientific communication and knowledge organization.

Contribution

The paper introduces new AI-driven approaches for scientific writing, method evaluation, and knowledge management in analytical chemistry.

Findings

AI can improve clarity in scholarly communication through AI-assisted tools.

New evaluation frameworks like i-metrics and AI-Delphi are proposed for method assessment.

Analytical chemistry could help develop an 'Analytics of Intelligent Systems' by evaluating AI models.

Abstract

Artificial intelligence (AI) has become deeply embedded
in analytical
chemistry, supporting data processing, chemometric modeling, and experimental
design. Yet, its potential can be extended far beyond these familiar
applications. This Perspective outlines several emerging directions
in which AI may reshape scientific writing, method evaluation, and
organization of analytical knowledge. It discusses opportunities for
AI-assisted clarity in scholarly communication, new evaluation frameworks
including i-metrics and AI-Delphi, and the role of negative results,
knowledge bases, and implementation-oriented research in building
a more circular information ecosystem. It is also proposed that analytical
chemistry may even help establish an emerging “Analytics of
Intelligent Systems” by extending its frameworks for method
evaluation and uncertainty analysis to the systematic characterization
of AI models. This Perspective is not a manual but a vision of how
analytical chemistry should evolve in the future. Ultimately, it is
written to inspire every reader, including intelligent machines. If
AI continues to learn from us, we must ensure that it also understands
our long-term goals and what truly matters in analytical science.

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