{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T16:42:13Z","timestamp":1793292133250,"version":"4.5.0"},"reference-count":71,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"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>Hematologic malignancies, including acute myeloid leukemia, myelodysplastic syndromes, lymphoma, and multiple myeloma, are characterized by profound biological heterogeneity and highly dynamic treatment trajectories that conventional, static prognostic systems incompletely capture. Cellular therapies, such as chimeric antigen receptor T-cell therapy and hematopoietic stem cell transplantation, offer potentially curative options for relapsed or refractory disease, yet outcomes remain highly variable, and management decisions regarding conditioning intensity, lymphodepletion, immunosuppression, and toxicity surveillance continue to be largely protocol-driven rather than individually adapted. Artificial intelligence (AI) and machine learning (ML) have demonstrated substantial promise in diagnostic support, prognostic stratification, and multimodal data integration across hematologic malignancies, but existing models remain predominantly static and related to pre-treatment in orientation, limiting their utility for real-time clinical guidance. This review summarizes current AI applications in hematologic oncology; critically compares the strengths, limitations, and clinical applicability of major AI model classes, including traditional machine learning, deep learning, multimodal integrative frameworks, reinforcement learning, digital twins, and emerging foundation models and large language models; and proposes an adaptive, multimodal paradigm. We examine key enabling technologies and address the clinical, regulatory, ethical, and implementation challenges that must be resolved before these systems can be deployed at the bedside. We argue that the central challenge facing the field is no longer whether AI can predict outcomes, but whether it can actively guide real-time therapeutic decisions, and that achieving this transition will require interdisciplinary collaboration, prospective validation, and governance frameworks capable of ensuring interpretability, equity, and clinical trustworthiness.<\/jats:p>","DOI":"10.3390\/cancers18162598","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T11:02:11Z","timestamp":1786532531000},"page":"2598","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["From Prediction to Intervention: Artificial Intelligence for Adaptive Response and Toxicity Modeling in Cellular Therapies for Hematologic Malignancies"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5888-3473","authenticated-orcid":false,"given":"Behzad","family":"Amoozgar","sequence":"first","affiliation":[{"name":"Department of Bone Marrow Transplant and Cellular Therapy, Loma Linda University Health, Loma Linda, CA 92350, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayrton","family":"Bangolo","sequence":"additional","affiliation":[{"name":"Department of Hematology and Oncology, Hackensack Meridian John Theurer Cancer Center, Hackensack, NJ 07047, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1624-4870","authenticated-orcid":false,"given":"Danielle C.","family":"Thor","sequence":"additional","affiliation":[{"name":"Department of Blood and Marrow Transplant and Cellular Therapy, Fox Chase Cancer Center, Philadelphia, PA 19111, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shibhani","family":"Rajanna","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Hackensack Meridian Health, Palisades Medical Center, North Bergen, NJ 07047, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shareif","family":"Abdelwahab","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Cleveland Clinic Foundation, Cleveland, OH 44195, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed S.","family":"Mohamed","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Hackensack Meridian Health, Palisades Medical Center, North Bergen, NJ 07047, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charlene","family":"Mansour","sequence":"additional","affiliation":[{"name":"Rutgers New Jersey Medical School, Newark, NJ 07103, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1896-7151","authenticated-orcid":false,"given":"Syed Usman","family":"Ehsanullah","sequence":"additional","affiliation":[{"name":"Department of Hematology Oncology, University Medical Center, El Paso, TX 79905, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101325","DOI":"10.1016\/j.blre.2025.101325","article-title":"Prognostic performance of machine learning in predicting haematological cancer outcomes: Systematic review and meta-analysis","volume":"74","author":"Alem","year":"2025","journal-title":"Blood Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1053\/j.seminhematol.2024.11.002","article-title":"The potential promise of machine learning in myelodysplastic syndrome","volume":"62","author":"Visconte","year":"2025","journal-title":"Semin. 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