{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T15:17:39Z","timestamp":1785511059193,"version":"3.56.0"},"reference-count":45,"publisher":"Georg Thieme Verlag KG","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Clin Colon Rectal Surg"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Colorectal cancer (CRC) is the third most commonly diagnosed malignancy worldwide. Prognosis is significantly worsened in patients with colorectal liver metastases (CRLM), whose management requires a multidisciplinary approach encompassing diagnosis, systemic therapy, surgery, and surveillance. Artificial intelligence (AI) offers the potential to improve treatment processes and outcomes across all aspects of CRLM care. This review summarizes current and future applications of AI throughout the CRLM treatment continuum. In diagnostics, radiomics-based AI models have demonstrated improved sensitivity in detecting small or ambiguous liver lesions, supporting radiologist interpretation, and improving efficiency. Similarly, AI models are increasingly employed to predict systemic treatment response, using deep learning (DL) to extract imaging-derived features that correlate with genomic and histopathologic profiles relevant to therapy selection. In surgical planning, AI tools can assist in preoperative preparation and optimization by measuring tumor volume and transection planes. Intraoperatively, computer vision and augmented reality are emerging to support tumor localization, margin assessment, and real-time anatomical navigation. Postoperatively, advanced AI models can integrate clinical, radiologic, and molecular data to stratify recurrence risk and inform individualized follow-up strategies. Despite its promise, clinical translation of AI in CRLM remains limited by the retrospective nature of many studies, challenges with external validation, and limitations in the interpretability of model decisions. Still, AI has the potential to be a transformative tool in the treatment of CRLM by supporting precision, standardization, and personalization across the treatment spectrum.<\/jats:p>","DOI":"10.1055\/a-2769-1413","type":"journal-article","created":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T00:11:04Z","timestamp":1767053464000},"page":"235-241","source":"Crossref","is-referenced-by-count":1,"title":["Artificial Intelligence for the Treatment and Management of Colorectal Liver Metastases"],"prefix":"10.1055","volume":"39","author":[{"given":"Kaitlyn M.","family":"Weyman","sequence":"additional","affiliation":[{"name":"Department of General Surgery, Medical College of Wisconsin, Milwaukee, Wisconsin, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anai N.","family":"Kothari","sequence":"additional","affiliation":[{"name":"Division of Surgical Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin, United 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