The fast development of artificial intelligence (AI), especially generative AI models, is changing the environment of analytical chemistry. As classical method generation in analytical methods relies on manual trial-and-error methodology as well as statistical methods, generative AI is a new paradigm with automated generation of experimental methodology and optimization. In this paper, the authors discuss the use of generative AI-based technologies, including large language models (LLMs) and neural network-based generators, to create new, efficient, and customized methods of analysis. The paper examines existing applications, technology frameworks, and issues and offers a roadmap with regards to the future incorporation of generative AI into daily analytical processes. Finally, the review indicates the promise of generative AI to reinvent analytical chemistry as a highly empirical research area into a predictive, data-driven research area.
Yasir Fathi Mahmood (Wed,) studied this question.