{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T05:42:39Z","timestamp":1793943759365,"version":"4.8.0"},"reference-count":69,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T00:00:00Z","timestamp":1780012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100008884","name":"Edward P. Evans Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008884","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:p>Artificial intelligence (AI) is a transformative technology that has captivated the medical world with its potential to optimize cancer treatment and enhance precision oncology. In cancer diagnosis and treatment, various AI technologies have already provided high-level data examination and analytics that preceding innovations were not capable of. Cancer immunotherapy is a treatment that seeks to boost the immune system to recognize and eradicate tumors. It is a field that is constantly evolving, serving as a fertile environment where AI technologies can accelerate discovery and personalize its regimens. In recent years, AI has played an increased role in the optimization of immunotherapy delivery and drug development. Traditional machine learning and its subfield of deep learning algorithms have already impacted response prediction and related tasks, such as patient stratification for immune checkpoint blockade treatment and identifying potent T-cells in the laboratory to develop effective cellular therapies. Additionally, recently developed technologies such as generative AI (gen AI) and foundation models have expanded upon traditional AI algorithms with new applications such as treatment plan generation and adverse event prediction. As innovations such as agentic AI and the model context protocol (MCP) become increasingly available, efficiency and success in immunotherapy development and delivery could further improve. That said, some challenges must be overcome for AI to reach its full potential in immunotherapy. These include concerns related to data quality control, patient safety, and addressing ethical dilemmas. In this article, we briefly review available state-of-the-art AI technologies for immunotherapy and highlight their capabilities. Then, we examine the current AI applications in immunotherapy including cell therapies, checkpoint inhibitors, and cancer vaccines, covering a diverse array of technologies over a wide range of applications. We analyze the datasets used, performance metrics, and downstream tasks, and highlight existing limitations. Subsequently, we discuss some of the obstacles that have prevented AI from routine clinical adoption. Finally, we envision the future of AI in immunotherapy that may include a framework involving an orchestration of multiple specialized AI agents with a human in the loop.<\/jats:p>","DOI":"10.3389\/fimmu.2026.1777580","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T06:03:09Z","timestamp":1780034589000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Artificial intelligence for optimization of immunotherapy: current applications and transformative potential"],"prefix":"10.3389","volume":"17","author":[{"given":"Ali","family":"Tarhini","sequence":"first","affiliation":[{"name":"Machine Learning Department, H. Lee Moffitt Cancer Center and Research Institute","place":["Tampa, FL, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Palak","family":"Dave","sequence":"additional","affiliation":[{"name":"Machine Learning Department, H. Lee Moffitt Cancer Center and Research Institute","place":["Tampa, FL, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shari","family":"Pilon-Thomas","sequence":"additional","affiliation":[{"name":"Immunology Department, H. Lee Moffitt Cancer Center and Research Institute","place":["Tampa, FL, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Issam","family":"El Naqa","sequence":"additional","affiliation":[{"name":"Machine Learning Department, H. Lee Moffitt Cancer Center and Research Institute","place":["Tampa, FL, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,29]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1093\/bjrai\/ubaf015","article-title":"General artificial intelligence for the diagnosis and treatment of cancer: the rise of foundation models","volume":"2","author":"Tarhini","year":"2025","journal-title":"BJR|Artificial Intell"},{"key":"B2","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1186\/s12943-025-02369-9","article-title":"Current AI technologies in cancer diagnostics and treatment","volume":"24","author":"Tiwari","year":"2025","journal-title":"Mol Cancer"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2025.1633422","article-title":"Editorial: Artificial intelligence for cancer immunotherapy","volume":"16","author":"El Naqa","year":"2025","journal-title":"Front Immunol"},{"key":"B4","doi-asserted-by":"publisher","first-page":"20250120","DOI":"10.3390\/diseases13010024","article-title":"Integrating AI into cancer immunotherapy-a narrative review of current applications and future directions","volume":"13","author":"Olawade","year":"2025","journal-title":"Diseases"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1016\/j.annonc.2025.03.006","article-title":"Artificial intelligence entering the pathology arena in oncology: current applications and future perspectives","volume":"36","author":"Marra","year":"2025","journal-title":"Ann Oncol"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1038\/s43018-022-00436-4","article-title":"Artificial intelligence in histopathology: enhancing cancer research and clinical oncology","volume":"3","author":"Shmatko","year":"2022","journal-title":"Nat Cancer"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20190855","article-title":"Artificial intelligence: reshaping the practice of radiological sciences in the 21st century","volume":"93","author":"El Naqa","year":"2020","journal-title":"Br J Radiol"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1038\/s41388-023-02826-z","article-title":"Translation of AI into oncology clinical practice","volume":"42","author":"El Naqa","year":"2023","journal-title":"Oncogene"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-023-06555-x","article-title":"A foundation model for generalizable disease detection from retinal images","volume":"622","author":"Zhou","year":"2023","journal-title":"Nature"},{"key":"B10","article-title":"Agentic ai for scientific discovery: A survey of progress, challenges, and future directions","volume-title":"arXiv preprint arXiv:250308979","author":"Gridach","year":"2025"},{"key":"B11","article-title":"Mcp safety audit: Llms with the model context protocol allow major security exploits","volume-title":"arXiv preprint arXiv:250403767","author":"Radosevich","year":"2025"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.3390\/curroncol29050247","article-title":"Immune checkpoint inhibitors in cancer therapy","volume":"29","author":"Shiravand","year":"2022","journal-title":"Curr Oncol"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1038\/s41577-020-0306-5","article-title":"A guide to cancer immunotherapy: from T cell basic science to clinical practice","volume":"20","author":"Waldman","year":"2020","journal-title":"Nat Rev Immunol"},{"key":"B14","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1038\/s41419-024-06976-0","article-title":"Natural killer cells at the frontline in the fight against cancer","volume":"15","author":"Coenon","year":"2024","journal-title":"Cell Death Dis"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1038\/s43018-024-00772-7","article-title":"LORIS robustly predicts patient outcomes with immune checkpoint blockade therapy using common clinical, pathologic and genomic features","volume":"5","author":"Chang","year":"2024","journal-title":"Nat Cancer"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-024-03398-5","article-title":"Prediction of checkpoint inhibitor immunotherapy efficacy for cancer using routine blood tests and clinical data","volume":"31","author":"Yoo","year":"2025","journal-title":"Nat Med"},{"key":"B17","first-page":"2025.03. 05.25323445","article-title":"irAE-GPT: Leveraging large language models to identify immune-related adverse events in electronic health records and clinical trial datasets","volume-title":"medRxiv","author":"Bejan","year":"2025"},{"key":"B18","doi-asserted-by":"publisher","first-page":"20250505","DOI":"10.1101\/2025.05.01.25326820","article-title":"Generalizable AI predicts immunotherapy outcomes across cancers and treatments","author":"Shen","year":"2025","journal-title":"medRxiv"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-024-08378-w","article-title":"A vision-language foundation model for precision oncology","volume":"638","author":"Xiang","year":"2025","journal-title":"Nature"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.3389\/fmmed.2023.1250508","article-title":"Elaborating the potential of artificial intelligence in automated CAR-T cell manufacturing","volume":"3","author":"Backel","year":"2023","journal-title":"Front Mol Med"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106084","article-title":"RCMNet: a deep learning model assists CAR-T therapy for leukemia","volume":"150","author":"Zhang","year":"2022","journal-title":"Comput Biol Med"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.1038\/s41422-024-00936-1","article-title":"CAR-Toner: an AI-driven approach for CAR tonic signaling prediction and optimization","volume":"34","author":"Qiu","year":"2024","journal-title":"Cell Res"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-025-03532-x","article-title":"An inflammatory biomarker signature of response to CAR-T cell therapy in non-Hodgkin lymphoma","volume":"31","author":"Raj","year":"2025","journal-title":"Nat Med"},{"key":"B24","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1038\/s41587-024-02318-9","article-title":"Author correction: Identification of clinically relevant T cell receptors for personalized T cell therapy using combinatorial algorithms","volume":"43","author":"Petremand","year":"2025","journal-title":"Nat Biotechnol"},{"key":"B25","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1038\/s41698-023-00470-0","article-title":"Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes for prediction of prognosis in resected colon cancer","volume":"7","author":"Lim","year":"2023","journal-title":"NPJ Precis Oncol"},{"key":"B26","first-page":"2025.06. 16.660016","article-title":"Learning human T cell behaviors through generative AI embeddings of T cell receptors","volume-title":"bioRxiv","author":"Chen","year":"2025"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2025.1550252","article-title":"OnmiMHC: a machine learning solution for UCEC tumor vaccine development through enhanced peptide-MHC binding prediction","volume":"16","author":"Jian","year":"2025","journal-title":"Front Immunol"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.7507\/1001-5515.202405024","article-title":"Prediction of MHC II antigen peptide-T cell receptors binding based on foundation model","volume":"41","author":"Xu","year":"2024","journal-title":"Sheng Wu Yi Xue Gong Cheng Xue Za Zhi"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.1177\/14230380251316788","article-title":"External validation of a serum tumor marker algorithm for early prediction of no durable benefit to immunotherapy in metastastic non-small cell lung carcinoma","volume":"47","author":"Schuurbiers","year":"2025","journal-title":"Tumour Biol"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.1001\/jamaoncol.2024.5356","article-title":"Deep learning model for predicting immunotherapy response in advanced non-small cell lung cancer","volume":"11","author":"Rakaee","year":"2025","journal-title":"JAMA Oncol"},{"key":"B31","doi-asserted-by":"publisher","first-page":"11065","DOI":"10.1038\/s41598-023-38076-y","article-title":"Radiomics approaches to predict PD-L1 and PFS in advanced non-small cell lung patients treated with immunotherapy: a multi-institutional study","volume":"13","author":"Yolchuyeva","year":"2023","journal-title":"Sci Rep"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.1136\/jitc-2020-002118","article-title":"Non-invasive measurement of PD-L1 status and prediction of immunotherapy response using deep learning of PET\/CT images","volume":"9","author":"Mu","year":"2021","journal-title":"J Immunother Cancer"},{"key":"B33","article-title":"Benchmarking open-source language models for efficient ques-tion answering in industrial applications Mahaman Sanoussi Yahaya Alassan, Jessica L\u00f3pez Espejel, Merieme Bouhandi","volume-title":"arXiv preprint arXiv:240613713","author":"Dahhane","year":"2024"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.3174\/ajnr.A7488","article-title":"Radiomics-based machine learning for outcome prediction in a multicenter phase II study of programmed death-ligand 1 inhibition immunotherapy for glioblastoma","volume":"43","author":"George","year":"2022","journal-title":"AJNR Am J Neuroradiol"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0252537","article-title":"Prediction of outcome in patients with non-small cell lung cancer treated with second line PD-1\/PDL-1 inhibitors based on clinical parameters: results from a prospective, single institution study","volume":"16","author":"Rounis","year":"2021","journal-title":"PloS One"},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.7150\/thno.50283","article-title":"Tumor immune profiles noninvasively estimated by FDG PET with deep learning correlate with immunotherapy response in lung adenocarcinoma","volume":"10","author":"Park","year":"2020","journal-title":"Theranostics"},{"key":"B37","doi-asserted-by":"crossref","DOI":"10.1136\/jitc-2024-SITC2024.0861","article-title":"861 Multimodal foundation model of human lung tumors identifies tertiary lymphoid structures (TLS) and reveals novel therapeutic targets that promote anti-tumor immune response","volume-title":"BMJ specialist journals","author":"Guo","year":"2024"},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2020.01900","article-title":"Biomarkers of the response to immune checkpoint inhibitors in metastatic urothelial carcinoma","volume":"11","author":"Chen","year":"2020","journal-title":"Front Immunol"},{"key":"B39","doi-asserted-by":"publisher","DOI":"10.1002\/cam4.3957","article-title":"Development and validation of MRI-based deep learning models for prediction of microsatellite instability in rectal cancer","volume":"10","author":"Zhang","year":"2021","journal-title":"Cancer Med"},{"key":"B40","doi-asserted-by":"publisher","DOI":"10.1158\/1078-0432.CCR-20-2415","article-title":"Using machine learning algorithms to predict immunotherapy response in patients with advanced melanoma","volume":"27","author":"Johannet","year":"2021","journal-title":"Clin Cancer Res"},{"key":"B41","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1186\/s40644-025-00837-5","article-title":"Development and validation of MRI-derived deep learning score for non-invasive prediction of PD-L1 expression and prognostic stratification in head and neck squamous cell carcinoma","volume":"25","author":"Ding","year":"2025","journal-title":"Cancer Imaging"},{"key":"B42","doi-asserted-by":"crossref","DOI":"10.1200\/JCO.2025.43.16_suppl.1577","article-title":"Biologically interpretable pathomics-driven transformer model with self-supervised training for outcome prediction of immunotherapy in non-small cell lung cancer","volume-title":"American society of clinical oncology","author":"Li","year":"2025"},{"key":"B43","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1009883","article-title":"In vitro machine learning-based CAR T immunological synapse quality measurements correlate with patient clinical outcomes","volume":"18","author":"Naghizadeh","year":"2022","journal-title":"PloS Comput Biol"},{"key":"B44","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1186\/s12964-020-00617-7","article-title":"The role of immunological synapse in predicting the efficacy of chimeric antigen receptor (CAR) immunotherapy","volume":"18","author":"Liu","year":"2020","journal-title":"Cell Commun Signal"},{"key":"B45","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.109137","article-title":"The transformative potential of AI-driven CRISPR-Cas9 genome editing to enhance CAR T-cell therapy","volume":"182","author":"Boretti","year":"2024","journal-title":"Comput Biol Med"},{"key":"B46","doi-asserted-by":"publisher","DOI":"10.1038\/s41422-023-00789-0","article-title":"Tuning charge density of chimeric antigen receptor optimizes tonic signaling and CAR-T cell fitness","volume":"33","author":"Chen","year":"2023","journal-title":"Cell Res"},{"key":"B47","doi-asserted-by":"publisher","DOI":"10.1111\/imr.13232","article-title":"Charging CAR by electrostatic power","volume":"320","author":"Wang","year":"2023","journal-title":"Immunol Rev"},{"key":"B48","doi-asserted-by":"publisher","DOI":"10.1016\/j.trecan.2021.10.007","article-title":"Exploiting T cell signaling to optimize engineered T cell therapies","volume":"8","author":"Wang","year":"2022","journal-title":"Trends Cancer"},{"key":"B49","doi-asserted-by":"publisher","first-page":"S127","DOI":"10.1016\/j.jcyt.2025.03.249","article-title":"Accelerating CAR-T manufacturing: Establishing an AI and automation-driven analytical development platform","volume":"27","author":"Lebrun","year":"2025","journal-title":"Cytotherapy"},{"key":"B50","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcyt.2025.05.003","article-title":"Artificial intelligence, machine learning, and digitalization systems in the cell and gene therapy sector: a guidance document from the ISCT industry committees","author":"Di Cerbo","year":"2025","journal-title":"Cytotherapy"},{"key":"B51","doi-asserted-by":"crossref","DOI":"10.1200\/JCO.2025.43.16_suppl.2516","article-title":"A novel application of deep learning (DL)-based MRI with liquid biomarkers for immune effector cell-associated neurotoxicity syndrome (ICANS) after chimeric antigen receptor (CAR) T-cell therapy","volume-title":"American society of clinical oncology","author":"Tringale","year":"2025"},{"key":"B52","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2025.18906","article-title":"Pathologist-read vs AI-driven assessment of tumor-infiltrating lymphocytes in melanoma","volume":"8","author":"Aung","year":"2025","journal-title":"JAMA Netw Open"},{"key":"B53","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1186\/s12967-025-06363-z","article-title":"Multi-cohort study in gastric cancer to develop CT-based radiomic models to predict pathological response to neoadjuvant immunotherapy","volume":"23","author":"Huang","year":"2025","journal-title":"J Transl Med"},{"key":"B54","doi-asserted-by":"publisher","first-page":"20250315","DOI":"10.1136\/jitc-2024-011149","article-title":"Voxel-level radiomics and deep learning for predicting pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant immunotherapy and chemotherapy","volume":"13","author":"Zhang","year":"2025","journal-title":"J Immunother Cancer"},{"key":"B55","doi-asserted-by":"publisher","first-page":"20240903","DOI":"10.1136\/jitc-2024-009348","article-title":"Non-invasive multimodal CT deep learning biomarker to predict pathological complete response of non-small cell lung cancer following neoadjuvant immunochemotherapy: a multicenter study","volume":"12","author":"Ye","year":"2024","journal-title":"J Immunother Cancer"},{"key":"B56","first-page":"2024.10. 01.616063","article-title":"scChat: A large language model-powered co-pilot for contextualized single-cell RNA sequencing analysis","volume-title":"bioRxiv","author":"Lu","year":"2024"},{"key":"B57","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-024-07441-w","article-title":"A whole-slide foundation model for digital pathology from real-world data","volume":"630","author":"Xu","year":"2024","journal-title":"Nature"},{"key":"B58","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2021.3095381","article-title":"ProtTrans: Toward understanding the language of life through self-supervised learning","volume":"44","author":"Elnaggar","year":"2022","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"B59","article-title":"Towards an AI co-scientist","volume-title":"arXiv preprint arXiv:250218864","author":"Gottweis","year":"2025"},{"key":"B60","doi-asserted-by":"publisher","DOI":"10.1002\/cam4.3935","article-title":"Artificial intelligence in oncology: Path to implementation","volume":"10","author":"Chua","year":"2021","journal-title":"Cancer Med"},{"key":"B61","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20220934","article-title":"AI and machine learning ethics, law, diversity, and global impact","volume":"96","author":"Drabiak","year":"2023","journal-title":"Br J Radiol"},{"key":"B62","doi-asserted-by":"publisher","first-page":"1138","DOI":"10.1038\/s41467-024-55259-x","article-title":"Intricacies of human-AI interaction in dynamic decision-making for precision oncology","volume":"16","author":"Niraula","year":"2025","journal-title":"Nat Commun"},{"key":"B63","doi-asserted-by":"publisher","DOI":"10.1007\/s40615-024-02057-2","article-title":"Bridging health disparities in the data-driven world of artificial intelligence: A narrative review","author":"Murphy","year":"2024","journal-title":"J Racial Ethn Health Disparities"},{"key":"B64","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: The convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat Med"},{"key":"B65","doi-asserted-by":"publisher","DOI":"10.1038\/538020a","article-title":"Can we open the black box of AI","volume":"538","author":"Castelvecchi","year":"2016","journal-title":"Nature"},{"key":"B66","article-title":"The dark secret at the heart of AI","volume-title":"MIT technology review","author":"Knight","year":"2017"},{"key":"B67","article-title":"Our machines now have knowledge we\u2019ll never understand","volume-title":"Wired","author":"Weinberger","year":"2017"},{"key":"B68","article-title":"The Malicious use of artificial intelligence: Forecasting, prevention, and mitigation","volume-title":"arXiv preprint arXiv:180207228","author":"Brundage","year":"2018"},{"key":"B69","article-title":"Llmcarbon: Modeling the end-to-end carbon footprint of large language models","volume-title":"arXiv preprint arXiv:230914393","author":"Faiz","year":"2023"}],"container-title":["Frontiers in Immunology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fimmu.2026.1777580\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T06:03:19Z","timestamp":1780034599000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fimmu.2026.1777580\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,29]]},"references-count":69,"alternative-id":["10.3389\/fimmu.2026.1777580"],"URL":"https:\/\/doi.org\/10.3389\/fimmu.2026.1777580","relation":{},"ISSN":["1664-3224"],"issn-type":[{"value":"1664-3224","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,29]]},"article-number":"1777580"}}