Mosquera Orgueira, AdrianAdrianMosquera OrgueiraPerez Encinas, Manuel MateoManuel MateoPerez EncinasDiaz Varela, NicolasNicolasDiaz VarelaWang, Yu-HungYu-HungWangMora, ElviraElviraMoraDiaz-Beya, MarinaMarinaDiaz-BeyaMontoro, Maria JuliaMaria JuliaMontoroPomares Marin, HelenaHelenaPomares MarinRamos Ortega, FernandoFernandoRamos OrtegaTormo, MarMarTormoJerez, AndresAndresJerezNomdedeu, JosepJosepNomdedeude Miguel Sanchez, CarlosCarlosde Miguel SanchezArenillas, LeonorLeonorArenillasCarcel, PaulaPaulaCarcelCedena Romero, Maria TeresaMaria TeresaCedena RomeroXicoy Cirici, BlancaBlancaXicoy CiriciRivero Arango, EugeniaEugeniaRivero ArangoDel Orbe Barreto, Rafael AndrésRafael AndrésDel Orbe BarretoBenlloch, LuisLuisBenllochCHIEN-CHIN LINHWEI-FANG TIENPérez Míguez, CarlosCarlosPérez MíguezCrucitti, DavideDavideCrucittiDíez Campelo, MaríaMaríaDíez CampeloValcárcel, DavidDavidValcárcel2024-03-292024-03-292024-02-2700071048https://www.scopus.com/record/display.uri?eid=2-s2.0-85186562544&doi=10.1111%2fbjh.19341&origin=inward&txGid=32d097b1b9cd53fa26e3f572a1766ecehttps://scholars.lib.ntu.edu.tw/handle/123456789/641594Chronic myelomonocytic leukaemia (CMML) is a rare haematological disorder characterized by monocytosis and dysplastic changes in myeloid cell lineages. Accurate risk stratification is essential for guiding treatment decisions and assessing prognosis. This study aimed to validate the Artificial Intelligence Prognostic Scoring System for Myelodysplastic Syndromes (AIPSS-MDS) in CMML and to assess its performance compared with traditional scores using data from a Spanish registry (n = 1343) and a Taiwanese hospital (n = 75). In the Spanish cohort, the AIPSS-MDS accurately predicted overall survival (OS) and leukaemia-free survival (LFS), outperforming the Revised-IPSS score. Similarly, in the Taiwanese cohort, the AIPSS-MDS demonstrated accurate predictions for OS and LFS, showing superiority over the IPSS score and performing better than the CPSS and molecular CPSS scores in differentiating patient outcomes. The consistent performance of the AIPSS-MDS across both cohorts highlights its generalizability. Its adoption as a valuable tool for personalized treatment decision-making in CMML enables clinicians to identify high-risk patients who may benefit from different therapeutic interventions. Future studies should explore the integration of genetic information into the AIPSS-MDS to further refine risk stratification in CMML and improve patient outcomes.enAIPSS-MDS; CMML; MDS; artificial intelligence; leukaemia; prognosis[SDGs]SDG3journal article10.1111/bjh.19341384112502-s2.0-85186562544