Abstract
Artificial intelligence (AI) has provided viable methods for retrieving, organizing, and analyzing mass spectrometry (MS) data in various applications. However, several challenges remain as this technique is still in its early, preliminary stages. Critical limitations include the need for more effective methods for identification, quantification, and interpretation to ensure rapid and accurate results. Recently, high-throughput MS data have been leveraged to advance machine learning (ML) techniques, particularly in matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS and MS imaging (MSI). The accuracy of AI models is intricately linked to the sampling techniques used in MALDI and MALDI imaging measurements. With the help of artificial neural networks, traditional barriers are being overcome, accelerating data acquisition for different applications. AI-driven analysis of chemical specificity and spatial mapping in two-dimensional datasets has gained significant attention, highlighting its potential impact. This review focuses on recent AI applications, particularly supervised ML in MALDI-TOF MS and MALDI-MSI data analysis. Additionally, this review provides an overview of sample preparation methods and sampling techniques essential for ensuring high-quality data in deep learning-based models.