{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T04:25:11Z","timestamp":1770524711474,"version":"3.49.0"},"reference-count":136,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T00:00:00Z","timestamp":1748304000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>As a major worldwide health concern, influenza still requires precise modeling of flu dynamics and efficient treatment approaches. Deep learning architectures are increasingly being applied to address the complexities of influenza dynamics and treatment optimization, which remain critical global health challenges. This review explores the utilization of deep learning methods, such as Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), transformer architectures, and large language models (LLMs), in modeling influenza virus behavior and enhancing therapeutic strategies. The dynamic nature of influenza viruses, characterized by rapid mutation rates and the emergence of new strains, complicates the development of effective treatments and vaccines. In other words, the discovery of effective treatments and vaccines is severely hampered by the dynamic character of flu viruses, their fast rates of mutation, and the appearance of novel strains. Traditional epidemiological models often fall short due to their reliance on manual data interpretation and limited capacity to analyze large datasets. In contrast, deep learning offers a more automated and objective approach, capable of uncovering intricate patterns within extensive flu-related data, including genetic sequences and patient records. The application of deep learning to comprehend flu dynamics and improve treatment strategies is examined in this review paper. Moreover, this paper discussed relevant research findings, and future directions in leveraging deep learning for improved understanding and management of influenza outbreaks, ultimately aiming for more personalized treatment regimens and enhanced public health responses.<\/jats:p>","DOI":"10.3389\/frai.2025.1521886","type":"journal-article","created":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T15:52:21Z","timestamp":1748361141000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep learning architectures for influenza dynamics and treatment optimization: a comprehensive review"],"prefix":"10.3389","volume":"8","author":[{"given":"Adane","family":"Adugna","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Desalegn","family":"Abebaw","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abtie","family":"Abebaw","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammed","family":"Jemal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,5,27]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1186\/s13321-022-00623-6","article-title":"Designing optimized drug candidates with generative adversarial network","volume":"14","author":"Abbasi","year":"2022","journal-title":"J. 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Rep."},{"key":"ref54","doi-asserted-by":"publisher","first-page":"101938","DOI":"10.1016\/j.artmed.2020.101938","article-title":"GANs for medical image analysis","volume":"109","author":"Kazeminia","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref55","year":"2025"},{"key":"ref56","doi-asserted-by":"publisher","first-page":"164","DOI":"10.3389\/fpubh.2020.00164","article-title":"Generative adversarial networks and its applications in biomedical informatics","volume":"8","author":"Lan","year":"2020","journal-title":"Front. 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Soc."},{"key":"ref63","doi-asserted-by":"publisher","first-page":"43250","DOI":"10.3390\/molecules25143250","article-title":"Relevant applications of generative adversarial networks in drug design and discovery: molecular De novo design, dimensionality reduction, and De novo peptide and protein design","volume":"25","author":"Lin","year":"2020","journal-title":"Molecules"},{"key":"ref64","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1007\/s41666-024-00167-4","article-title":"DDE: deep dynamic epidemiological modeling for infectious illness development forecasting in multi-level geographic entities","volume":"8","author":"Liu","year":"2024","journal-title":"J. Healthcare Inf. Res."},{"key":"ref65","doi-asserted-by":"publisher","first-page":"19453","DOI":"10.1007\/s00521-022-07158-9","article-title":"A novel multi-step forecasting strategy for enhancing deep learning models\u2019 performance","volume":"34","author":"Livieris","year":"2022","journal-title":"Neural Comput. 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Eng."},{"key":"ref86","first-page":"265","article-title":"Ethical, societal and legal issues in deep learning for healthcare","volume-title":"Deep learning in biology and medicine","author":"Panigutti","year":"2021"},{"key":"ref87","doi-asserted-by":"publisher","first-page":"442","DOI":"10.3348\/kjr.2021.0048","article-title":"Key principles of clinical validation, device approval, and insurance coverage decisions of","volume":"22","author":"Park","year":"2021","journal-title":"Artif. Intell."},{"key":"ref88","doi-asserted-by":"publisher","first-page":"3105","DOI":"10.1038\/s41467-023-38809-7","article-title":"Epidemiological inference for emerging viruses using segregating sites","volume":"14","author":"Park","year":"2023","journal-title":"Nat. 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Sci."},{"key":"ref95","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12065-020-00540-3","article-title":"Convolutional neural networks in medical image understanding: a survey","volume":"15","author":"Sarvamangala","year":"2022","journal-title":"Evol. Intel."},{"key":"ref96","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1080\/17460441.2021.1918098","article-title":"Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade","volume":"16","author":"Serafim","year":"2021","journal-title":"Expert Opin. Drug Discov."},{"key":"ref97","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1093\/aje\/kwad107","article-title":"Deep learning for epidemiologists: an introduction to neural networks","volume":"192","author":"Serghiou","year":"2023","journal-title":"Am. J. Epidemiol."},{"key":"ref98","doi-asserted-by":"publisher","first-page":"47862","DOI":"10.1038\/s41467-024-47862-9","article-title":"Seasonal antigenic prediction of influenza a H3N2 using machine learning","volume":"15","author":"Shah","year":"2024","journal-title":"Nat. Commun."},{"key":"ref99","author":"Shen","year":"2024"},{"key":"ref100","doi-asserted-by":"publisher","first-page":"132306","DOI":"10.1016\/j.physd.2019.132306","article-title":"Fundamentals of recurrent neural network (RNN) and Long short-term memory (LSTM) network","volume":"404","author":"Sherstinsky","year":"2020","journal-title":"Physica D Nonlinear Phenomena"},{"key":"ref101","volume-title":"A deep learning approach for efficient eye influenza detection","author":"Singh","year":"2024"},{"key":"ref102","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/978-981-97-8019-8_6","article-title":"Transformer architectures","volume-title":"Deep learning through the prism of tensors","author":"Singh","year":"2024"},{"key":"ref103","doi-asserted-by":"publisher","first-page":"13722","DOI":"10.1038\/s41598-023-40922-y","article-title":"A separable temporal convolutional networks based deep learning technique for discovering antiviral medicines","volume":"13","author":"Singh","year":"2023","journal-title":"Sci. Rep."},{"key":"ref104","doi-asserted-by":"publisher","first-page":"208","DOI":"10.2174\/1574893618666230227105703","article-title":"Drug design and disease diagnosis: the potential of deep learning models in biology","volume":"18","author":"Sreeraman","year":"2023","journal-title":"Curr. Bioinforma."},{"key":"ref105","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1016\/j.ebiom.2019.08.024","article-title":"Forecasting influenza activity using self-adaptive AI model and multi-source data in Chongqing, China","volume":"47","author":"Su","year":"2019","journal-title":"EBioMedicine"},{"key":"ref106","volume-title":"Simulating recombinant sequence date to evaluate and improve computational methods of multiple sequence alignment and recombinant identification","author":"Swanepoel","year":"2023"},{"key":"ref107","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1038\/s41398-020-00957-5","article-title":"Machine learning for effectively avoiding overfitting is a crucial strategy for the genetic prediction of polygenic psychiatric phenotypes","volume":"10","author":"Takahashi","year":"2020","journal-title":"Transl. Psychiatry"},{"key":"ref108","doi-asserted-by":"publisher","first-page":"2335","DOI":"10.1007\/s00530-022-00960-4","article-title":"A survey on the interpretability of deep learning in medical diagnosis","volume":"28","author":"Teng","year":"2022","journal-title":"Multimedia Systems"},{"key":"ref109","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1038\/s41586-023-06617-0","article-title":"Learning from prepandemic data to forecast viral escape","volume":"622","author":"Thadani","year":"2023","journal-title":"Nature"},{"key":"ref110","doi-asserted-by":"publisher","first-page":"25281","DOI":"10.36948\/ijfmr.2024.v06i04.25281","article-title":"Explainable AI: developing interpretable deep learning models for medical diagnosis","volume":"6","author":"Thakur","year":"2024","journal-title":"Int. J. Multidisciplinary Res."},{"key":"ref111","doi-asserted-by":"publisher","first-page":"1203874","DOI":"10.3389\/fdgth.2023.1203874","article-title":"Comparison of pretrained transformer-based models for influenza and COVID-19 detection using social media text data in Saskatchewan, Canada","volume":"5","author":"Tian","year":"2023","journal-title":"Front. Digital Health"},{"key":"ref112","doi-asserted-by":"publisher","first-page":"858","DOI":"10.3390\/ijerph19031858","article-title":"The prediction of influenza-like illness and respiratory disease using LSTM and ARIMA","volume":"19","author":"Tsan","year":"2022","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"ref113","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1038\/s41573-019-0024-5","article-title":"Applications of machine learning in drug discovery and development","volume":"18","author":"Vamathevan","year":"2019","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref114","doi-asserted-by":"publisher","first-page":"1569","DOI":"10.1007\/s11030-021-10225-3","article-title":"Convolutional neural networks (CNNs): concepts and applications in pharmacogenomics","volume":"25","author":"Vaz","year":"2021","journal-title":"Mol. Divers."},{"key":"ref115","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/978-981-15-2286-4_2","article-title":"Modeling the stochastic dynamics of influenza epidemics with vaccination control, and the maximum likelihood estimation of model parameters","volume-title":"Mathematical modelling in health, social and applied sciences","author":"Wanduku","year":"2020"},{"key":"ref116","doi-asserted-by":"publisher","first-page":"e1007883","DOI":"10.1371\/journal.pcbi.1007883","article-title":"Differentiation of cytopathic effects (CPE) induced by influenza virus infection using deep convolutional neural networks (CNN)","volume":"16","author":"Wang","year":"2020","journal-title":"PLoS Comput. Biol."},{"key":"ref117","year":"2023"},{"key":"ref118","doi-asserted-by":"publisher","first-page":"a038489","DOI":"10.1101\/cshperspect.a038489","article-title":"The ecology and evolution of influenza viruses","volume":"10","author":"Wille","year":"2020","journal-title":"Cold Spring Harb. Perspect. Med."},{"key":"ref119","year":"2025"},{"key":"ref120","doi-asserted-by":"publisher","first-page":"121202","DOI":"10.1016\/j.eswa.2023.121202","article-title":"Effective LSTMs with seasonal-trend decomposition and adaptive learning and niching-based backtracking search algorithm for time series forecasting","volume":"236","author":"Wu","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref121","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/9997669","article-title":"A deep learning approach for predicting antigenic variation of influenza a H3N2","volume":"2021","author":"Xia","year":"2021","journal-title":"Comput. Math. Methods Med."},{"key":"ref122","doi-asserted-by":"publisher","first-page":"108975","DOI":"10.1016\/j.knosys.2022.108975","article-title":"A lightweight ensemble discriminator for generative adversarial networks","volume":"250","author":"Xie","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref123","doi-asserted-by":"publisher","first-page":"e44238","DOI":"10.2196\/44238","article-title":"Deep-learning model for influenza prediction from multisource heterogeneous data in a megacity: model development and evaluation","volume":"25","author":"Yang","year":"2023","journal-title":"J. Med. Internet Res."},{"key":"ref124","doi-asserted-by":"publisher","first-page":"2840","DOI":"10.1038\/s41598-018-21059-9","article-title":"Comparing the similarity and difference of three influenza surveillance systems in China","volume":"8","author":"Yang","year":"2018","journal-title":"Sci. Rep."},{"key":"ref125","doi-asserted-by":"publisher","first-page":"7857","DOI":"10.1038\/s41467-023-43715-z","article-title":"TransformEHR: transformer-based encoder-decoder generative model to enhance prediction of disease outcomes using electronic health records","volume":"14","author":"Yang","year":"2023","journal-title":"Nat. Commun."},{"key":"ref126","author":"Yilmaz","year":"2023"},{"key":"ref127","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1093\/bioinformatics\/btaa901","article-title":"VirPreNet: a weighted ensemble convolutional neural network for the virulence prediction of influenza a virus using all eight segments","volume":"37","author":"Yin","year":"2021","journal-title":"Bioinformatics"},{"key":"ref128","doi-asserted-by":"publisher","first-page":"3497","DOI":"10.1109\/TCBB.2021.3108971","article-title":"IAV-CNN: a 2D convolutional neural network model to predict antigenic variants of influenza a virus","volume":"19","author":"Yin","year":"2022","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref129","author":"Yu","year":"2022"},{"key":"ref130","doi-asserted-by":"publisher","first-page":"105263","DOI":"10.1016\/j.bspc.2023.105263","article-title":"Reviewing methods of deep learning for intelligent healthcare systems in genomics and biomedicine","volume":"86","author":"Zafar","year":"2023","journal-title":"Biomed Signal Proc. Control"},{"key":"ref131","doi-asserted-by":"publisher","first-page":"106495","DOI":"10.1016\/j.cmpb.2021.106495","article-title":"DeepFlu: a deep learning approach for forecasting symptomatic influenza a infection based on pre-exposure gene expression","volume":"213","author":"Zan","year":"2022","journal-title":"Comput. Methods Prog. Biomed."},{"key":"ref132","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1109\/RBME.2018.2864254","article-title":"Learning for personalized medicine: a comprehensive review from a deep learning perspective","volume":"12","author":"Zhang","year":"2019","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref133","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1017\/S0950268818000705","article-title":"Multi-step prediction for influenza outbreak by an adjusted long short-term memory","volume":"146","author":"Zhang","year":"2018","journal-title":"Epidemiol. Infect."},{"key":"ref134","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1186\/s12879-023-08025-1","article-title":"Study on the prediction effect of a combined model of SARIMA and LSTM based on SSA for influenza in Shanxi Province, China","volume":"23","author":"Zhao","year":"2023","journal-title":"BMC Infect. Dis."},{"key":"ref135","doi-asserted-by":"publisher","first-page":"2335","DOI":"10.1186\/s12889-022-14299-y","article-title":"Study on the influence of meteorological factors on influenza in different regions and predictions based on an LSTM algorithm","volume":"22","author":"Zhu","year":"2022","journal-title":"BMC Public Health"},{"key":"ref136","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1186\/s12859-019-3131-8","article-title":"Attention-based recurrent neural network for influenza epidemic prediction","volume":"20","author":"Zhu","year":"2019","journal-title":"BMC Bioinf."}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2025.1521886\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T15:52:26Z","timestamp":1748361146000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2025.1521886\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,27]]},"references-count":136,"alternative-id":["10.3389\/frai.2025.1521886"],"URL":"https:\/\/doi.org\/10.3389\/frai.2025.1521886","relation":{},"ISSN":["2624-8212"],"issn-type":[{"value":"2624-8212","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,27]]},"article-number":"1521886"}}