Artificial Intelligence in Analytical Chemistry: Towards Yet Undiscovered Opportunities

Искусственный интеллект в аналитической химии: на пути к еще не открытым возможностям
Paweł Mateusz Nowak
2026-02-19

AI-Delphianalytical chemistryartificial intelligencechemometric modelinguncertainty analysis
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.
1
AI in analytical chemistry already supports data processing, chemometric modeling, and experimental design, but substantial opportunities remain beyond these established applications.
2
Analytical chemistry could contribute to an emerging “Analytics of Intelligent Systems” by applying method-evaluation and uncertainty-analysis frameworks to systematically characterize AI models.
3
Emerging AI applications could reshape scientific writing, method evaluation, and the organization of analytical knowledge.
4
Negative results, knowledge bases, and implementation-oriented research could support a more circular and effective information ecosystem in analytical chemistry.
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The Perspective proposes AI-assisted scholarly clarity and new evaluation frameworks, including i-metrics and AI-Delphi.

the future evolution of analytical chemistry through the integration of artificial intelligence

emerging opportunities and frameworks for applying AI to scientific communication, method evaluation, knowledge organization, and systematic characterization of AI models

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2026-02-19
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Paweł Mateusz Nowak
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