{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T15:32:57Z","timestamp":1786635177543,"version":"build-2736575974"},"reference-count":92,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T00:00:00Z","timestamp":1783468800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T00:00:00Z","timestamp":1783468800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Obesity Reviews"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Artificial intelligence (AI) enables automated, high\u2010throughput adiposity quantification, offering refined risk stratification for women with overweight and obesity. We systematically reviewed and meta\u2010analyzed studies evaluating AI\u2010based segmentation of visceral, subcutaneous, and total fat in adult women populations (BMI: 25\u201329.9 and \u2265\u200930\u2009kg\/m\n                    <jats:sup>2<\/jats:sup>\n                    ), searching MEDLINE, CENTRAL, Embase, Scopus, ScienceDirect, and Web of Science from inception to January 15, 2025, in accordance with PRISMA guidelines. Studies were included if they (1) focused on adult women (\u2265\u200918\u2009years) undergoing MRI, CT, or DXA\u2010based fat quantification; (2) employed automated or conventional radiomics segmentation; and (3) reported quantitative adiposity metrics (volumes or areas). Two reviewers independently extracted data, assessed quality via QUADAS\u20102, and synthesized results using inverse\u2010variance and sample size\u2013weighted random\u2010effects models. Of 13,098 records screened, 38 studies (12,815 women) met inclusion criteria. AI methods demonstrated high segmentation accuracy, exemplified by 3D\u2010Unet (Dice score: 0.97 [95% CI: 0.96\u20130.98]) and &gt;\u200999% faster processing than manual methods (0.05 vs. 46.5\u2009min\/patient;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001). However, the primary finding was extreme between\u2010study heterogeneity, indicating limited reproducibility of adiposity measurements across studies. Substantial heterogeneity undermined pooled volumetric estimates: subcutaneous fat volume averaged 22,536\u2009cm\n                    <jats:sup>3<\/jats:sup>\n                    (\n                    <jats:italic>I<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    \u2009=\u200999.9%), visceral fat 3615\u2009cm\n                    <jats:sup>3<\/jats:sup>\n                    (\n                    <jats:italic>I<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    \u2009=\u2009100%), and total fat 35,062\u2009cm\n                    <jats:sup>3<\/jats:sup>\n                    (\n                    <jats:italic>I<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    \u2009=\u200999.8%), with prediction intervals spanning anatomically implausible ranges. This variability stemmed from inconsistent acquisition parameters, segmentation protocols, and modality\u2010specific differences, with DXA\u2010derived subcutaneous fat volumes 5.6\u2010fold higher than MRI\u2010derived estimates and CT attenuation thresholds spanning 244 HU (Hounsfield Units). Subgroup analyses indicated 72% of variance arose from technical rather than anthropometric factors. While AI enables highly efficient and accurate segmentation at an individual level, protocol heterogeneity and underrepresentation of diverse populations limit clinical generalizability. Standardized imaging protocols, harmonized analytic frameworks, and inclusive sampling are essential to translate AI's precision into clinically reliable adiposity metrics for women's health.\n                  <\/jats:p>","DOI":"10.1111\/obr.70187","type":"journal-article","created":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T10:13:06Z","timestamp":1783505586000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Precision or Paradox? 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