{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T18:48:59Z","timestamp":1792349339978,"version":"4.4.0"},"reference-count":262,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T00:00:00Z","timestamp":1792281600000},"content-version":"vor","delay-in-days":47,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Spatial transcriptomics (STs) measures gene expression while retaining the location of transcripts or cells within tissue sections. Unlike bulk RNA sequencing, which averages expression across cell populations, and single-cell RNA sequencing, which generally requires tissue dissociation, ST links molecular profiles to tissue architecture. This spatial information supports the analysis of tissue heterogeneity, cellular organization, and potential intercellular interactions. The spatially resolved nature of ST has unlocked diverse applications in fields such as cancer, neuroscience, reproductive biology, and plant biology. In parallel, the development of publicly accessible databases and repositories has facilitated large-scale, multi-cohort comparisons and benchmarking. The rapid expansion and increasing complexity of these datasets have driven the adoption of machine learning (ML), deep learning (DL), and artificial intelligence (AI) techniques. These computational tools support a range of tasks, including data pre-processing, image segmentation, spatial domain identification, and pattern recognition. Moreover, they are instrumental in integrative multi-omics analyses and predictive modeling. This AI-focused review provides necessary background on ST technologies and databases and evaluates the use of statistical, ML, and DL methods across the analytical workflow, from image and sequencing-data preprocessing to spatial analysis and LLM-assisted interpretation. We compile key resources related to datasets, AI-driven analytical frameworks, and ST data analysis pipelines, and discuss current challenges, limitations, and future directions for AI-based tools in spatial omics.<\/jats:p>","DOI":"10.1093\/bib\/bbag569","type":"journal-article","created":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T17:59:46Z","timestamp":1792346386000},"source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence in spatial transcriptomics: a review of methods, datasets, and directions"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1765-3644","authenticated-orcid":false,"given":"Neelam","family":"Sharma","sequence":"first","affiliation":[{"name":"Department of Medical Biosciences, Ume\u00e5 University , 901 85, Ume\u00e5 ,","place":["Sweden"]},{"name":"Science for Life Laboratory (SciLifeLab), Ume\u00e5 University , 901 85, Ume\u00e5 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