AI-Driven Computational Enhancement of Mass Spectrometry Imaging: Detection Sensitivity, Spatial Resolution, Molecular Coverage, and Acquisition Throughput

Mass spectrometry imaging (MSI) is a powerful analytical technique that enables spatially resolved molecular profiling of tissues, providing insights into biological complexity. While advances in hardware and sample preparation improve MSI performance in sensitivity, spatial resolution, molecular coverage, and acquisition throughput, these gains are often constrained by trade-offs, where optimizing one metric compromises another. Computational strategies have emerged as a cost-effective means of alleviating these physicochemical trade-offs. Specifically, computational approaches can improve detection sensitivity and coverage by learning spectral and spatial patterns for signal detection, denoising, and recovery; achieve reported spatial enhancement factors of 2–20× through super-resolution reconstruction and multimodal fusion; expand annotation coverage by integrating chemical priors with learned spectral–spatial features; and accelerate acquisition through employing sparse sampling strategies followed by computational reconstruction, reducing the required number of measurements by up to 80%. This review critically examines recent computational advancements, with a focus on artificial intelligence (AI) approaches, while addressing ongoing challenges and proposing solutions. It can serve as a cross-disciplinary bridge by offering analytical chemists guidance on using computational tools to optimize the data acquisition and interpretation, and providing data scientists with MSI-specific context for developing targeted algorithms. We anticipate that advanced computational methods, particularly AI, will support next-generation MSI performance enhancement and enable new biological insights.