{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T06:46:20Z","timestamp":1790318780502,"version":"4.1.0"},"reference-count":84,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T00:00:00Z","timestamp":1708387200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Artificial intelligence (AI) is emerging as a discipline capable of providing significant added value in Medicine, in particular in radiomic, imaging analysis, big dataset analysis, and also for generating virtual cohort of patients. However, in coping with chronic myeloid leukemia (CML), considered an easily managed malignancy after the introduction of TKIs which strongly improved the life expectancy of patients, AI is still in its infancy. Noteworthy, the findings of initial trials are intriguing and encouraging, both in terms of performance and adaptability to different contexts in which AI can be applied. Indeed, the improvement of diagnosis and prognosis by leveraging biochemical, biomolecular, imaging, and clinical data can be crucial for the implementation of the personalized medicine paradigm or the streamlining of procedures and services. In this review, we present the state of the art of AI applications in the field of CML, describing the techniques and objectives, and with a general focus that goes beyond Machine Learning (ML), but instead embraces the wider AI field. The present scooping review spans on publications reported in Pubmed from 2003 to 2023, and resulting by searching \u201cchronic myeloid leukemia\u201d and \u201cartificial intelligence\u201d. The time frame reflects the real literature production and was not restricted. We also take the opportunity for discussing the main pitfalls and key points to which AI must respond, especially considering the critical role of the \u2018human\u2019 factor, which remains key in this domain.<\/jats:p>","DOI":"10.3390\/cancers16050848","type":"journal-article","created":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T11:50:07Z","timestamp":1708429807000},"page":"848","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Artificial Intelligence-Based Management of Adult Chronic Myeloid Leukemia: Where Are We and Where Are We Going?"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3494-2624","authenticated-orcid":false,"given":"Simona","family":"Bernardi","sequence":"first","affiliation":[{"name":"Department of Clinical and Experimental Sciences, University of Brescia, 25123 Brescia, Italy"},{"name":"CREA\u2014Centro di Ricerca Emato-Oncologica AIL, ASST Spedali Civili of Brescia, 25123 Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8429-3570","authenticated-orcid":false,"given":"Mauro","family":"Vallati","sequence":"additional","affiliation":[{"name":"School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4716-9925","authenticated-orcid":false,"given":"Roberto","family":"Gatta","sequence":"additional","affiliation":[{"name":"Department of Clinical and Experimental Sciences, University of Brescia, 25123 Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1001\/jama.2017.18391","article-title":"Big Data and Machine Learning in Health Care","volume":"319","author":"Beam","year":"2018","journal-title":"JAMA"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"38","DOI":"10.4103\/jpi.jpi_53_18","article-title":"Artificial Intelligence and Digital Pathology: Challenges and Opportunities","volume":"9","author":"Pantanowitz","year":"2018","journal-title":"J. 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