{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T10:18:34Z","timestamp":1787653114749,"version":"build-2736575974"},"reference-count":138,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T00:00:00Z","timestamp":1787616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Curr. Issues Mol. Biol.","CIMB"],"abstract":"<jats:p>The histopathology area is being redefined with the use of artificial intelligence (AI), providing robust tools to help bridge cellular morphology, functional assays, and multi-omics data in stem cell research. The ability of stem cells to undergo self-renewal and differentiation is key in regenerative medicine, but their research requires the careful characterization of morphological and molecular phenotypic traits. Traditional histopathology is invaluable, but its application can be limited by inter-observer variability and restricted scalability. These limitations are circumvented by AI-based techniques, such as machine learning and deep learning, which are capable of classifying cells, performing quantitative morphometry, and forecasting stem cell behavior. Adding AI to genomics, proteomics, and metabolomics will contribute to the further identification of biomarkers and pathways that regulate stem cell fate. This convergence provides new possibilities for precision medicine, personalized therapies, and translational uses like drug discovery and disease modeling. However, its potential has not yet been realized because of the existing difficulties in data quality, variability, regulatory control, and ethical issues, especially in terms of the transparency and justice of AI systems. Emphasized areas for the future include explainable AI, federated learning, and a multimodal framework that integrates imaging, sequencing, and clinical data. Interdisciplinary partnerships and adequate regulatory frameworks will help AI-enabled histopathology reshape stem cell studies and speed up the process of translating regenerative medicine into clinical applications.<\/jats:p>","DOI":"10.3390\/cimb48090859","type":"journal-article","created":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T09:04:30Z","timestamp":1787648670000},"page":"859","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence-Powered Histopathology in Stem Cell Research: Bridging Morphology, Function, and Omics"],"prefix":"10.3390","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4144-8120","authenticated-orcid":false,"given":"Mashael Saleh","family":"Al-Toub","sequence":"first","affiliation":[{"name":"Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Saud University, Riyadh P.O. Box 60169, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1186\/s12943-025-02240-x","article-title":"From morphology to single-cell molecules: High-resolution 3D histology in biomedicine","volume":"24","author":"Xu","year":"2025","journal-title":"Mol. Cancer"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1186\/s13287-019-1165-5","article-title":"Stem cells: Past, present, and future","volume":"10","author":"Zakrzewski","year":"2019","journal-title":"Stem Cell Res. Ther."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"100423","DOI":"10.1016\/j.jpi.2025.100423","article-title":"An international study of stain variability in histopathology using qualitative and quantitative analysis","volume":"17","author":"Dunn","year":"2025","journal-title":"J. Pathol. 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