{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T16:45:06Z","timestamp":1791996306716,"version":"4.4.0"},"reference-count":77,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T00:00:00Z","timestamp":1756944000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cells"],"abstract":"<jats:p>Artificial Intelligence and machine learning are increasingly used to interrogate complex biological data. This systematic review evaluates their application to multi-omics for the molecular characterization of hematological malignancies, an area with unmet clinical need. We searched PubMed, Embase, Institute of Electrical and Electronics Engineers Xplore, and Web of Science from January 2015 to December 2024. Two reviewers screened records, extracted data, and used a modified appraisal emphasizing explainability, performance, reproducibility, and ethics. From 2847 records, 89 studies met inclusion criteria. Studies focused on acute myeloid leukemia (34), acute lymphoblastic leukemia (23), and multiple myeloma (18). Other hematological diseases were less frequently studied. Methods included Support Vector Machines, Random Forests, and deep learning (28, 25, and 24 studies). Multi-omics integration was reported in 23 studies. External validation occurred in 31 studies, and explainability in 19. The median diagnostic area under the curve was 0.87 (interquartile range 0.81 to 0.94); deep learning reached 0.91 but offered the least explainability. Artificial Intelligence and machine learning show promise for molecular characterization, yet gaps in validation, interpretability, and standardization remain. Priorities include external validation, interpretable modeling, harmonized evaluation, and standardized reporting with shared benchmarks to enable safe, reproducible clinical translation.<\/jats:p>","DOI":"10.3390\/cells14171385","type":"journal-article","created":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T15:31:21Z","timestamp":1756999881000},"page":"1385","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Machine Learning for Multi-Omics Characterization of Blood Cancers: A Systematic Review"],"prefix":"10.3390","volume":"14","author":[{"given":"Sultan Qalit","family":"Alhamrani","sequence":"first","affiliation":[{"name":"Tabuk Poison Control and Forensic Medicinal Chemistry Center, Ministry of Health, Tabuk 47915, Saudi Arabia"},{"name":"Department of Biotechnology, Faculty of Science, Cairo University, Giza 12613, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5828-7129","authenticated-orcid":false,"given":"Graham Roy","family":"Ball","sequence":"additional","affiliation":[{"name":"Intelligent Omics Ltd., Biocity, Pennyfoot Street, Nottingham NG1 1GF, UK"},{"name":"Medical Technology Research Centre, Anglia Ruskin University, Chelmsford Campus, Bishop Hall Lane, Chelmsford CM1 1SQ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed A.","family":"El-Sherif","sequence":"additional","affiliation":[{"name":"Department of Chemistry, Faculty of Science, Cairo University, Giza 12613, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5258-0586","authenticated-orcid":false,"given":"Shaza","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Faculty of Biotechnology, October University for Modern Sciences and Arts, Giza 12451, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5851-7369","authenticated-orcid":false,"given":"Nahla O.","family":"Mousa","sequence":"additional","affiliation":[{"name":"Department of Biotechnology, Faculty of Science, Cairo University, Giza 12613, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shahad Ali","family":"Alghorayed","sequence":"additional","affiliation":[{"name":"Faculty of Biotechnology, October University for Modern Sciences and Arts, Giza 12451, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nader Atallah","family":"Alatawi","sequence":"additional","affiliation":[{"name":"Faculty of Biotechnology, October University for Modern Sciences and Arts, Giza 12451, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Albalawi Mohammed","family":"Ali","sequence":"additional","affiliation":[{"name":"King Fahad Specialist Hospital, Tabuk Ministry of Health, Tabuk 47717, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fahad Abdullah","family":"Alqahtani","sequence":"additional","affiliation":[{"name":"King Fahad Specialist Hospital, Tabuk Ministry of Health, Tabuk 47717, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7511-9204","authenticated-orcid":false,"given":"Refaat M.","family":"Gabre","sequence":"additional","affiliation":[{"name":"Department of Biotechnology, Faculty of Science, Cairo University, Giza 12613, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sorokin, M., Borisov, N., Kuzmin, D., Gudkov, A., Zolotovskaia, M., Garazha, A., and Buzdin, A. (2021). Algorithmic Annotation of Functional Roles for Components of 3044 Human Molecular Pathways. Front. Genet., 12.","DOI":"10.3389\/fgene.2021.617059"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1007\/s40258-025-00949-w","article-title":"The Cost Effectiveness of Genomic Medicine in Cancer Control: A Systematic Literature Review","volume":"23","author":"Bourke","year":"2025","journal-title":"Appl. Health Econ. Health Policy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"101019","DOI":"10.1016\/j.blre.2022.101019","article-title":"Artificial intelligence in hematological diagnostics: Game changer or gadget?","volume":"58","author":"Walter","year":"2023","journal-title":"Blood Rev."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Elshoeibi, A.M., Badr, A., Elsayed, B., Metwally, O., Elshoeibi, R., Elhadary, M.R., Elshoeibi, A., Attya, M.A., Khadadah, F., and Alshurafa, A. (2023). Integrating AI and ML in Myelodysplastic Syndrome Diagnosis: State-of-the-Art and Future Prospects. Cancers, 16.","DOI":"10.3390\/cancers16010065"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Tsagiopoulou, M., and Gut, I.G. (2024). Machine learning and multi-omics data in chronic lymphocytic leukemia: The future of precision medicine?. Front. Genet., 14.","DOI":"10.3389\/fgene.2023.1304661"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e41766","DOI":"10.1097\/MD.0000000000041766","article-title":"Big data analytics and machine learning in hematology: Transformative insights, applications and challenges","volume":"104","author":"Obeagu","year":"2025","journal-title":"Medicine"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1093\/jalm\/jfac108","article-title":"Hematology and machine learning","volume":"8","author":"Obstfeld","year":"2023","journal-title":"J. Appl. Lab. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s11306-006-0037-z","article-title":"Statistical strategies for avoiding false discoveries in metabolomics and related experiments","volume":"2","author":"Broadhurst","year":"2006","journal-title":"Metabolomics"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5008","DOI":"10.1016\/j.csbj.2021.09.001","article-title":"Machine learning and deep learning methods that use omics data for metastasis prediction","volume":"19","author":"Albaradei","year":"2021","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1186\/s12943-023-01736-8","article-title":"SWI\/SNF complexes in hematological malignancies: Biological implications and therapeutic opportunities","volume":"22","author":"Andrades","year":"2023","journal-title":"Mol. Cancer"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: An updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e047709","DOI":"10.1136\/bmjopen-2020-047709","article-title":"Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: The STARD-AI protocol","volume":"11","author":"Sounderajah","year":"2021","journal-title":"BMJ Open"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.trac.2013.04.011","article-title":"Multiple comparisons in mass-spectrometry-based-omics technologies","volume":"50","author":"Franceschi","year":"2013","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1198\/016214502753479248","article-title":"Comparison of discrimination methods for the classification of tumors using gene expression data","volume":"97","author":"Dudoit","year":"2002","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1056\/NEJMra1814259","article-title":"Machine learning in medicine","volume":"380","author":"Rajkomar","year":"2019","journal-title":"N. Engl. J. Med."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1182\/blood.2022015850","article-title":"International Consensus Classification of Myeloid Neoplasms and Acute Leukemias: Integrating morphologic, clinical, and genomic data","volume":"140","author":"Arber","year":"2022","journal-title":"Blood"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lee, J., Cho, S., Hong, S.-E., Kang, D., Choi, H., Lee, J.-M., Yoon, J.-H., Cho, B.-S., Lee, S., and Kim, H.-J. (2021). Integrative analysis of gene expression data by RNA sequencing for differential diagnosis of acute leukemia: Potential application of machine learning. Front. Oncol., 11.","DOI":"10.3389\/fonc.2021.717616"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jim\u00e9nez, C., Garrote-De-Barros, A., L\u00f3pez-Portugu\u00e9s, C., Hern\u00e1ndez-S\u00e1nchez, M., and D\u00edez, P. (2024). Characterization of Human B Cell Hematological Malignancies Using Protein-Based Approaches. Int. J. Mol. Sci., 25.","DOI":"10.3390\/ijms25094644"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Dunphy, K., O\u2019mahoney, K., Dowling, P., O\u2019gorman, P., and Bazou, D. (2021). Clinical Proteomics of Biofluids in Haematological Malignancies. Int. J. Mol. Sci., 22.","DOI":"10.3390\/ijms22158021"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1182\/bloodadvances.2018030726","article-title":"A parsimonious 3-gene signature predicts clinical outcomes in an acute myeloid leukemia multicohort study","volume":"3","author":"Wagner","year":"2019","journal-title":"Blood Adv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Katsenou, A., O\u2019farrell, R., Dowling, P., Heckman, C.A., O\u2019gorman, P., and Bazou, D. (2023). Using proteomics data to identify personalized treatments in multiple myeloma: A machine learning approach. Int. J. Mol. Sci., 24.","DOI":"10.3390\/ijms242115570"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2947","DOI":"10.1074\/mcp.M115.050245","article-title":"Machine Learning-based Classification of Diffuse Large B-cell Lymphoma Patients by Their Protein Expression Profiles","volume":"14","author":"Deeb","year":"2015","journal-title":"Mol. Cell. Proteom."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1038\/ng1201-365","article-title":"Minimum information about a microarray experiment (MIAME)\u2014Toward standards for microarray data","volume":"29","author":"Brazma","year":"2001","journal-title":"Nat. Genet."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1038\/nbt1360","article-title":"The minimum information about a genome sequence (MIGS) specification","volume":"26","author":"Field","year":"2008","journal-title":"Nat. Biotechnol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1038\/nbt1329","article-title":"The minimum information about a proteomics experiment (MIAPE)","volume":"25","author":"Taylor","year":"2007","journal-title":"Nat. Biotechnol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1852","DOI":"10.1038\/s41591-020-1089-8","article-title":"Precision medicine treatment in acute myeloid leukemia using prospective genomic profiling: Feasibility and preliminary efficacy of the Beat AML Master Trial","volume":"26","author":"Burd","year":"2020","journal-title":"Nat. Med."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1038\/nm.4439","article-title":"The molecular landscape of pediatric acute myeloid leukemia reveals recurrent structural alterations and age-specific mutational interactions","volume":"24","author":"Bolouri","year":"2018","journal-title":"Nat. Med."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"6077","DOI":"10.1182\/bloodadvances.2020002997","article-title":"Application of machine learning in the management of acute myeloid leukemia: Current practice and future prospects","volume":"4","author":"Eckardt","year":"2020","journal-title":"Blood Adv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"380","DOI":"10.3390\/hemato5040027","article-title":"The Role of Machine Learning in the Most Common Hematological Malignancies: A Narrative Review","volume":"5","author":"Perillo","year":"2024","journal-title":"Hemato"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1145\/3233231","article-title":"The mythos of model interpretability","volume":"61","author":"Lipton","year":"2018","journal-title":"Commun. ACM"},{"key":"ref_35","first-page":"39","article-title":"Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models","volume":"1","author":"Samek","year":"2017","journal-title":"ITU J. ICT Discov."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"57283","DOI":"10.1109\/ACCESS.2022.3176274","article-title":"Symptom based explainable artificial intelligence model for leukemia detection","volume":"10","author":"Hossain","year":"2022","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Gimeno, M., Jos\u00e9-En\u00e9riz, E.S., Villar, S., Agirre, X., Prosper, F., Rubio, A., and Carazo, F. (2022). Explainable artificial intelligence for precision medicine in acute myeloid leukemia. Front. Immunol., 13.","DOI":"10.3389\/fimmu.2022.977358"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"216793","DOI":"10.1016\/j.canlet.2024.216793","article-title":"Disparities in mortality risk after diagnosis of hematological malignancies in 185 countries: A global data analysis","volume":"595","author":"Yang","year":"2024","journal-title":"Cancer Lett."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Almeida, J.M., Castro, G.A., Machado-Neto, J.A., and Almeida, T.A. (2023). An explainable model to support the decision about the therapy protocol for AML. Brazilian Conference on Intelligent Systems, Springer.","DOI":"10.1007\/978-3-031-45368-7_28"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"127","DOI":"10.54097\/zJ4MnbWW","article-title":"Implementation of an AI-based MRD evaluation and prediction model for multiple myeloma","volume":"6","author":"Chen","year":"2024","journal-title":"Front. Comput. Intell. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Jiang, X., Hu, Z., Wang, S., and Zhang, Y. (2023). Deep Learning for Medical Image-Based Cancer Diagnosis. Cancers, 15.","DOI":"10.3390\/cancers15143608"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.3324\/haematol.2021.280209","article-title":"Deep learning applications in visual data for benign and malignant hematologic conditions: A systematic review and visual glossary","volume":"108","author":"Srisuwananukorn","year":"2023","journal-title":"Haematologica"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Manescu, P., Narayanan, P., Bendkowski, C., Elmi, M., Claveau, R., Pawar, V., Brown, B.J., Shaw, M., Rao, A., and Fernandez-Reyes, D. (2023). Detection of acute promyelocytic leukemia in peripheral blood and bone marrow with annotation-free deep learning. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-29160-4"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Kotsyfakis, S., Iliaki-Giannakoudaki, E., Anagnostopoulos, A., Papadokostaki, E., Giannakoudakis, K., Goumenakis, M., and Kotsyfakis, M. (2022). The application of machine learning to imaging in hematological oncology: A scoping review. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.1080988"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Morabito, F., Adornetto, C., Monti, P., Amaro, A., Reggiani, F., Colombo, M., Rodriguez-Aldana, Y., Tripepi, G., D\u2019aRrigo, G., and Vener, C. (2023). Genes selection using deep learning and explainable artificial intelligence for chronic lymphocytic leukemia predicting the need and time to therapy. Front. Oncol., 13.","DOI":"10.3389\/fonc.2023.1198992"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s10549-009-0378-1","article-title":"A validated gene expression profile for detecting clinical outcome in breast cancer using artificial neural networks","volume":"120","author":"Lancashire","year":"2010","journal-title":"Breast Cancer Res. Treat."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1016\/S1470-2045(16)00174-1","article-title":"SPAG5 as a prognostic biomarker and chemotherapy sensitivity predictor in breast cancer: A retrospective, integrated genomic, transcriptomic, and protein analysis","volume":"17","author":"Agarwal","year":"2016","journal-title":"Lancet Oncol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1111\/npqu.12080","article-title":"Dataism is Our New God","volume":"34","author":"Harari","year":"2017","journal-title":"New Perspect. Q."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1111\/bjh.16915","article-title":"Machine learning and artificial intelligence in hematology","volume":"192","author":"Shouval","year":"2021","journal-title":"Br. J. Haematol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1038\/s42256-021-00307-0","article-title":"Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans","volume":"3","author":"Roberts","year":"2021","journal-title":"Nat. Mach. Intell."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"4271","DOI":"10.1038\/s41388-021-01861-y","article-title":"How artificial intelligence might disrupt diagnostics in hematology in the near future","volume":"40","author":"Walter","year":"2021","journal-title":"Oncogene"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/s11899-023-00716-5","article-title":"Unlocking the Potential of Artificial Intelligence in Acute Myeloid Leukemia and Myelodysplastic Syndromes","volume":"19","author":"Alhajahjeh","year":"2024","journal-title":"Curr. Hematol. Malig. Rep."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Wang, S.-X., Huang, Z.-F., Li, J., Wu, Y., Du, J., and Li, T. (2024). Optimization of diagnosis and treatment of hematological diseases via artificial intelligence. Front. Med., 11.","DOI":"10.3389\/fmed.2024.1487234"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"4041","DOI":"10.1182\/blood-2021-149521","article-title":"Harmony Alliance provides a machine learning researching tool to predict the risk of relapse after first remission in AML patients treated without allogeneic haematopoietic stem cell transplantation","volume":"138","author":"Sobas","year":"2021","journal-title":"Blood"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1177\/1176935119835544","article-title":"Predicting complete remission of acute myeloid leukemia: Machine learning applied to gene expression","volume":"18","author":"Gal","year":"2019","journal-title":"Cancer Inform."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Cheng, Z.J., Li, H., Liu, M., Fu, X., Liu, L., Liang, Z., Gan, H., and Sun, B. (2024). Artificial intelligence reveals the predictions of hematological indexes in children with acute leukemia. BMC Cancer, 24.","DOI":"10.1186\/s12885-024-12646-3"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"100128","DOI":"10.1016\/j.apjon.2022.100128","article-title":"Development and validation of a machine learning model to predict venous thromboembolism among hospitalized cancer patients","volume":"9","author":"Meng","year":"2022","journal-title":"Asia-Pac. J. Oncol. Nurs."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.ajpath.2022.09.006","article-title":"Differential diagnosis of hematologic and solid tumors using targeted transcriptome and artificial intelligence","volume":"193","author":"Zhang","year":"2023","journal-title":"Am. J. Pathol."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Syed-Abdul, S., Firdani, R.-P., Chung, H.-J., Uddin, M., Hur, M., Park, J.H., Kim, H.W., Gradi\u0161ek, A., and Dovgan, E. (2020). Artificial intelligence-based models for screening of hematologic malignancies using cell population data. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-61247-0"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1111\/bjh.18343","article-title":"Integrating artificial intelligence into hematology training and practice: Opportunities, threats and proposed solutions","volume":"198","author":"Chai","year":"2022","journal-title":"Br. J. Haematol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/s11899-020-00575-4","article-title":"Artificial intelligence in hematology: Current challenges and opportunities","volume":"15","author":"Radakovich","year":"2020","journal-title":"Curr. Hematol. Malig. Rep."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1462","DOI":"10.1038\/s41592-023-01992-y","article-title":"Principles and challenges of modeling temporal and spatial omics data","volume":"20","author":"Velten","year":"2023","journal-title":"Nat. Methods"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Karathanasis, N., Papasavva, P.L., Oulas, A., and Spyrou, G.M. (2024). Combining clinical and molecular data for personalized treatment in acute myeloid leukemia: A machine learning approach. Comput. Methods Programs Biomed., 257.","DOI":"10.1016\/j.cmpb.2024.108432"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"e000255","DOI":"10.1136\/bmjonc-2023-000255","article-title":"Prospective evaluation of artificial intelligence (AI) applications for use in cancer pathways following diagnosis: A systematic review","volume":"3","author":"Macheka","year":"2024","journal-title":"BMJ Oncol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"7997","DOI":"10.1007\/s00432-023-04667-5","article-title":"An overview and a roadmap for artificial intelligence in hematology and oncology","volume":"149","author":"Altenbuchinger","year":"2023","journal-title":"J. Cancer Res. Clin. Oncol."},{"key":"ref_66","first-page":"20","article-title":"Advancements in Machine Learning (ML): Transforming the Future of Blood Cancer Detection and Outcome Prediction","volume":"20","author":"Roiss","year":"2024","journal-title":"Healthbook TIMES Oncol. Hematol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.aca.2020.10.038","article-title":"Multi-omics integration in biomedical research\u2014A metabolomics-centric review","volume":"1141","author":"Krumsiek","year":"2021","journal-title":"Anal. Chim. Acta"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Feldner-Busztin, D., Nisantzis, P.F., Edmunds, S.J., Boza, G., Racimo, F., Gopalakrishnan, S., Limborg, M.T., Lahti, L., de Polavieja, G.G., and Wren, J. (2023). Dealing with dimensionality: The application of machine learning to multi-omics data. Bioinformatics, 39.","DOI":"10.1093\/bioinformatics\/btad021"},{"key":"ref_69","first-page":"1","article-title":"Scientific large language models: A survey on biological & chemical domains","volume":"57","author":"Zhang","year":"2024","journal-title":"ACM Comput. Surv."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.pathol.2020.12.004","article-title":"Machine learning and augmented human intelligence use in histomorphology for haematolymphoid disorders","volume":"53","author":"Nanaa","year":"2021","journal-title":"Pathology"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Hansen, J., Jain, A.R., Nenov, P., Robinson, P.N., and Iyengar, R. (2024). From transcriptomics to digital twins of organ function. Front. Cell Dev. Biol., 12.","DOI":"10.3389\/fcell.2024.1240384"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1007\/s10142-024-01514-9","article-title":"Application of a high-throughput swarm-based deep neural network Algorithm reveals SPAG5 downregulation as a potential therapeutic target in adult AML","volume":"25","author":"Ajonu","year":"2025","journal-title":"Funct. Integr. Genomics"},{"key":"ref_73","first-page":"e2300021","article-title":"Synthetic data generation by artificial intelligence to accelerate research and precision medicine in hematology. JCO Clin","volume":"7","author":"Sala","year":"2023","journal-title":"Cancer Inform."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"100546","DOI":"10.1016\/j.cosrev.2023.100546","article-title":"Synthetic data generation: State of the art in health care domain","volume":"48","author":"Murtaza","year":"2023","journal-title":"Comput. Sci. Rev."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"100914","DOI":"10.1016\/j.blre.2021.100914","article-title":"The future of research in hematology: Integration of conventional studies with real-world data and artificial intelligence","volume":"54","author":"Passamonti","year":"2022","journal-title":"Blood Rev."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1186\/s12859-021-04368-1","article-title":"The challenges of explainable AI in biomedical data science","volume":"22","author":"Han","year":"2022","journal-title":"BMC Bioinform."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"111393","DOI":"10.1016\/j.ejrad.2024.111393","article-title":"Artificial intelligence and explanation: How, why, and when to explain black boxes","volume":"173","author":"Marcus","year":"2024","journal-title":"Eur. J. Radiol."}],"container-title":["Cells"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-4409\/14\/17\/1385\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:39:43Z","timestamp":1760035183000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-4409\/14\/17\/1385"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,4]]},"references-count":77,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["cells14171385"],"URL":"https:\/\/doi.org\/10.3390\/cells14171385","relation":{},"ISSN":["2073-4409"],"issn-type":[{"value":"2073-4409","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,4]]}}}