{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T18:52:01Z","timestamp":1781895121569,"version":"3.54.5"},"reference-count":61,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T00:00:00Z","timestamp":1678924800000},"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>Philadelphia-negative (Ph-) myeloproliferative neoplasms (MPNs) are a group of hematopoietic malignancies identified by clonal proliferation of blood cell lineages and encompasses polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF). The clinical and laboratory features of Philadelphia-negative MPNs are similar, making them difficult to diagnose, especially in the preliminary stages. Because treatment goals and progression risk differ amongst MPNs, accurate classification and prognostication are critical for optimal management. Artificial intelligence (AI) and machine learning (ML) algorithms provide a plethora of possible tools to clinicians in general, and particularly in the field of malignant hematology, to better improve diagnosis, prognosis, therapy planning, and fundamental knowledge. In this review, we summarize the literature discussing the application of AI and ML algorithms in patients with diagnosed or suspected Philadelphia-negative MPNs. A literature search was conducted on PubMed\/MEDLINE, Embase, Scopus, and Web of Science databases and yielded 125 studies, out of which 17 studies were included after screening. The included studies demonstrated the potential for the practical use of ML and AI in the diagnosis, prognosis, and genomic landscaping of patients with Philadelphia-negative MPNs.<\/jats:p>","DOI":"10.3390\/diagnostics13061123","type":"journal-article","created":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T02:40:11Z","timestamp":1678934411000},"page":"1123","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Applications of Artificial Intelligence in Philadelphia-Negative Myeloproliferative Neoplasms"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5983-0135","authenticated-orcid":false,"given":"Basel","family":"Elsayed","sequence":"first","affiliation":[{"name":"College of Medicine, QU Health, Qatar University, Doha 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2266-7784","authenticated-orcid":false,"given":"Amgad","family":"Elshoeibi","sequence":"additional","affiliation":[{"name":"College of Medicine, QU Health, Qatar University, Doha 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3075-9600","authenticated-orcid":false,"given":"Mohamed","family":"Elhadary","sequence":"additional","affiliation":[{"name":"College of Medicine, QU Health, Qatar University, Doha 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0562-8123","authenticated-orcid":false,"given":"Khaled","family":"Ferih","sequence":"additional","affiliation":[{"name":"College of Medicine, QU Health, Qatar University, Doha 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4927-9888","authenticated-orcid":false,"given":"Ahmed","family":"Elsabagh","sequence":"additional","affiliation":[{"name":"College of Medicine, QU Health, Qatar University, Doha 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alaa","family":"Rahhal","sequence":"additional","affiliation":[{"name":"Pharmacy Department, Heart Hospital, Hamad Medical Corporation (HMC), Doha 3050, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2984-0252","authenticated-orcid":false,"given":"Mohammad","family":"Abu-Tineh","sequence":"additional","affiliation":[{"name":"Hematology Section, Medical Oncology, National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation (HMC), Doha 3050, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Afana","sequence":"additional","affiliation":[{"name":"Hematology Section, Medical Oncology, National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation (HMC), Doha 3050, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed","family":"Abdulgayoom","sequence":"additional","affiliation":[{"name":"Hematology Section, Medical Oncology, National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation (HMC), Doha 3050, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1144-8076","authenticated-orcid":false,"given":"Mohamed","family":"Yassin","sequence":"additional","affiliation":[{"name":"Hematology Section, Medical Oncology, National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation (HMC), Doha 3050, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e123","DOI":"10.1182\/blood-2014-02-554634","article-title":"Integrated genomic analysis illustrates the central role of JAK-STAT pathway activation in myeloproliferative neoplasm pathogenesis","volume":"123","author":"Rampal","year":"2014","journal-title":"Blood"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.jbior.2018.11.007","article-title":"Heterogeneity in myeloproliferative neoplasms: Causes and consequences","volume":"71","author":"Mead","year":"2019","journal-title":"Adv. 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