{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T22:29:03Z","timestamp":1776464943807,"version":"3.51.2"},"reference-count":38,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2023,11,2]],"date-time":"2023-11-02T00:00:00Z","timestamp":1698883200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Artificial intelligence (AI) has attracted increasing attention as a tool for the detection and management of several medical conditions. Multiple myeloma (MM), a malignancy characterized by uncontrolled proliferation of plasma cells, is one of the most common hematologic malignancies, which relies on imaging for diagnosis and management. We aimed to review the current literature and trends in AI research of MM imaging. This study was performed according to the PRISMA guidelines. Three main concepts were used in the search algorithm, including \u201cartificial intelligence\u201d in \u201cradiologic examinations\u201d of patients with \u201cmultiple myeloma\u201d. The algorithm was used to search the PubMed, Embase, and Web of Science databases. Articles were screened based on the inclusion and exclusion criteria. In the end, we used the checklist for Artificial Intelligence in Medical Imaging (CLAIM) criteria to evaluate the manuscripts. We provided the percentage of studies that were compliant with each criterion as a measure of the quality of AI research on MM. The initial search yielded 977 results. After reviewing them, 14 final studies were selected. The studies used a wide array of imaging modalities. Radiomics analysis and segmentation tasks were the most popular studies (10\/14 studies). The common purposes of radiomics studies included the differentiation of MM bone lesions from other lesions and the prediction of relapse. The goal of the segmentation studies was to develop algorithms for the automatic segmentation of important structures in MM. Dice score was the most common assessment tool in segmentation studies, which ranged from 0.80 to 0.97. These studies show that imaging is a valuable data source for medical AI models and plays an even greater role in the management of MM.<\/jats:p>","DOI":"10.3390\/diagnostics13213372","type":"journal-article","created":{"date-parts":[[2023,11,2]],"date-time":"2023-11-02T09:16:21Z","timestamp":1698916581000},"page":"3372","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Current Status and Future of Artificial Intelligence in MM Imaging: A Systematic Review"],"prefix":"10.3390","volume":"13","author":[{"given":"Ehsan","family":"Alipour","sequence":"first","affiliation":[{"name":"Department of Radiology, Division of Musculoskeletal Imaging and Intervention, University of Washington, Seattle, WA 98195, USA"},{"name":"Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Atefe","family":"Pooyan","sequence":"additional","affiliation":[{"name":"Department of Radiology, Division of Musculoskeletal Imaging and Intervention, University of Washington, Seattle, WA 98195, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Firoozeh","family":"Shomal Zadeh","sequence":"additional","affiliation":[{"name":"Department of Radiology, Division of Musculoskeletal Imaging and Intervention, University of Washington, Seattle, WA 98195, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Azad Duke","family":"Darbandi","sequence":"additional","affiliation":[{"name":"Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL 60064, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5335-9429","authenticated-orcid":false,"given":"Pietro Andrea","family":"Bonaffini","sequence":"additional","affiliation":[{"name":"Department of Radiology, Papa Giovanni XXIII Hospital, 24127 Bergamo, Italy"},{"name":"School of Medicine, University Milano Bicocca, 20126 Milan, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8400-1593","authenticated-orcid":false,"given":"Majid","family":"Chalian","sequence":"additional","affiliation":[{"name":"Department of Radiology, Division of Musculoskeletal Imaging and Intervention, University of Washington, Seattle, WA 98195, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1001\/jama.2022.0003","article-title":"Diagnosis and Management of Multiple Myeloma: A Review","volume":"327","author":"Cowan","year":"2022","journal-title":"JAMA"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1148\/rg.2019180096","article-title":"Whole-Body Imaging of Multiple Myeloma: Diagnostic Criteria","volume":"39","author":"Carneiro","year":"2019","journal-title":"Radiographics"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e538-48","DOI":"10.1016\/S1470-2045(14)70442-5","article-title":"International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma","volume":"15","author":"Rajkumar","year":"2014","journal-title":"Lancet Oncol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Padala, S.A., Barsouk, A., and Barsouk, A. 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Mak., 20.","DOI":"10.1186\/s12911-020-01332-6"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/21\/3372\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:16:06Z","timestamp":1760130966000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/21\/3372"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,2]]},"references-count":38,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["diagnostics13213372"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics13213372","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,2]]}}}