{"doi":"10.1002/viw.20250119","title":"Artificial intelligence‐assisted theranostics for brain tumors: Advancements and future perspectives","abstract":"<jats:title>Abstract</jats:title>\n                  <jats:p>Brain tumors remain among the most devastating neurological disorders, with glioblastoma representing one of the most lethal forms. Despite advancements in clinical neuro‐oncology, accurate diagnosis, prognostication, and treatment personalization remain challenging due to tumor heterogeneity, anatomical complexity, and diagnostic limitations. Artificial intelligence (AI)‐assisted theranostics (AIT) have emerged as a transformative approach to managing brain tumors. This review comprehensively explores the applications of AI in brain tumor detection, classification, segmentation, prognosis, and therapy optimization. By leveraging machine learning, deep learning, and computer vision, AI can analyze vast amounts of imaging, clinical, and genomic data to extract hidden patterns and assist clinicians in decision‐making. Multimodal AI frameworks integrate magnetic resonance imaging (MRI), computed tomography (CT), histopathological, and molecular data to enable noninvasive prediction of biomarkers such as isocitrate dehydrogenase mutation, O6‐methylguanine–DNA methyltransferase (MGMT) methylation, and oncogenic pathways. Furthermore, AI enhances personalized therapy by predicting treatment response and refining intervention strategies. Cutting‐edge technologies, including convolutional neural networks, generative adversarial networks, and transformer models, have significantly improved image segmentation, tumor classification, and therapeutic planning. However, several challenges remain, including algorithm interpretability, regulatory approval, data diversity, and ethical considerations. Standardization of imaging protocols and external validation are essential for clinical adoption. In conclusion, AIT represents a paradigm shift in neuro‐oncology, promising faster diagnosis, more accurate tumor characterization, and precision‐guided treatment. With continued interdisciplinary collaboration, AI will likely redefine the standard of care for brain tumor patients in the near future.</jats:p>","journal":"VIEW","year":2025,"id":650219,"datarank":0.24141568686511508,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"self_citation_contribution":0.24141568686511508,"citation_network_contribution":0.0,"self_endowment_contribution":0.24141568686511508,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1691087,"name":"Lingyan Zhang","orcid":null,"position":1,"is_corresponding":false},{"id":1438063,"name":"Zia Ullah","orcid":"0000-0003-3027-1648","position":2,"is_corresponding":false},{"id":1669143,"name":"Yinghe Zhang","orcid":null,"position":3,"is_corresponding":false},{"id":1695327,"name":"Shengxue Wei","orcid":null,"position":4,"is_corresponding":false},{"id":1695328,"name":"Lihe Huang","orcid":null,"position":5,"is_corresponding":false},{"id":1095881,"name":"Bing Guo","orcid":"0000-0001-8998-2597","position":6,"is_corresponding":false},{"id":1691081,"name":"Mifang Li","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Artificial intelligence‐assisted theranostics for brain tumors: Advancements and future perspectives","abstract":"<jats:title>Abstract</jats:title>\n                  <jats:p>Brain tumors remain among the most devastating neurological disorders, with glioblastoma representing one of the most lethal forms. Despite advancements in clinical neuro‐oncology, accurate diagnosis, prognostication, and treatment personalization remain challenging due to tumor heterogeneity, anatomical complexity, and diagnostic limitations. Artificial intelligence (AI)‐assisted theranostics (AIT) have emerged as a transformative approach to managing brain tumors. This review comprehensively explores the applications of AI in brain tumor detection, classification, segmentation, prognosis, and therapy optimization. By leveraging machine learning, deep learning, and computer vision, AI can analyze vast amounts of imaging, clinical, and genomic data to extract hidden patterns and assist clinicians in decision‐making. Multimodal AI frameworks integrate magnetic resonance imaging (MRI), computed tomography (CT), histopathological, and molecular data to enable noninvasive prediction of biomarkers such as isocitrate dehydrogenase mutation, O6‐methylguanine–DNA methyltransferase (MGMT) methylation, and oncogenic pathways. Furthermore, AI enhances personalized therapy by predicting treatment response and refining intervention strategies. Cutting‐edge technologies, including convolutional neural networks, generative adversarial networks, and transformer models, have significantly improved image segmentation, tumor classification, and therapeutic planning. However, several challenges remain, including algorithm interpretability, regulatory approval, data diversity, and ethical considerations. Standardization of imaging protocols and external validation are essential for clinical adoption. In conclusion, AIT represents a paradigm shift in neuro‐oncology, promising faster diagnosis, more accurate tumor characterization, and precision‐guided treatment. With continued interdisciplinary collaboration, AI will likely redefine the standard of care for brain tumor patients in the near future.</jats:p>","is_dataset_classified":null,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19162232","pmcid":null,"openalex_id":"https://openalex.org/W4415150613","authors":[],"funders":[{"funder_name":"Basic and Applied Basic Research Foundation of Guangdong Province","grant_id":"2022A1515011781","title":null},{"funder_name":"Basic and Applied Basic Research Foundation of Guangdong Province","grant_id":"2024A1515220001","title":null},{"funder_name":"Basic and Applied Basic Research Foundation of Guangdong Province","grant_id":"2025A1515011922","title":null},{"funder_name":"Shenzhen Science and Technology Innovation Program","grant_id":"JCYJ20240813114628038","title":null}],"total_grants":4,"fwci":1.6056,"citation_percentile":0.84762908,"influential_citations":0,"citation_trend":[{"year":2026,"count":4}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://doi.org/10.1002/viw.20250119","host_type":"journal"},{"url":"https://doi.org/10.1002/viw.20250119","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/pdf/10.1002/VIW.20250119","host_type":"publisher"},{"url":"https://doaj.org/article/1b419d248d7c4a5ab3e393f80cc605d6","host_type":"repository"}],"fields_of_study":["Brain Tumor Detection and Classification","Radiomics and Machine Learning in Medical Imaging","Artificial Intelligence in Healthcare and Education"],"mesh_terms":[],"keywords":["Deep learning","Convolutional neural network","Glioblastoma","Personalization","Neuroimaging","Precision medicine","Standardization","Medical imaging"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-10T05:04:24.365326Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}