Abstract
The intersection of modern artificial intelligence (AI) and mass spectrometry (MS) is set to transform the MS-based "omics" research fields, particularly proteomics, metabolomics, lipidomics, and glycomics, enabling advancements across a wide range of domains, from health to environment and industrial biotechnology. Beginning with an overview of key challenges inherent in MS software pipelines, this personal perspective explores how AI-driven solutions can address them to enhance data processing, integration and interpretation. It proposes a paradigm shift in molecular identification and quantitation algorithms, leveraging AI to enable holistic interpretation of MS-based multiomics data. While centered on MS-based omics, this holistic AI-driven paradigm is also critical for connecting dynamic biochemical changes to genomics and transcriptomics contexts, reinforcing the integrative value of MS in multiomics research. Ultimately, this AI-driven approach could enhance efficiency, accuracy, and molecular breadth of coverage, deepening our systems-level understanding of biological processes and accelerating a myriad of biodiscoveries.