{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T22:58:08Z","timestamp":1778540288849,"version":"3.51.4"},"reference-count":61,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T00:00:00Z","timestamp":1762473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Viruses"],"abstract":"<jats:p>Artificial intelligence (AI) is increasingly transforming biomedical research and patient care by integrating complex biological, radiological, and healthcare information. In the field of viral respiratory infections, AI-driven approaches have shown great promise in elucidating the complexity of lung immune responses and the dynamic interplay between host and pathogen. Applications include predicting cytokine storm and acute respiratory distress syndrome (ARDS), integrating imaging findings with immunological and laboratory data, and identifying molecular and cellular signatures through single-cell and multi-omics analyses. Similar methodologies have been applied to influenza and respiratory syncytial virus (RSV), providing insights into the mechanisms distinguishing protective from maladaptive pulmonary immunity. This narrative review summarizes current evidence on how AI can evolve into a form of translational intelligence, capable of bridging mechanistic immunology with clinical application. The review explores AI-based models for disease severity prediction, patient stratification, and therapeutic response assessment, as well as emerging approaches in drug repurposing and vaccine response prediction. By integrating biological complexity with clinical context, AI offers new opportunities to uncover immune signatures predictive of antiviral or immunomodulatory efficacy and to guide personalized management strategies.<\/jats:p>","DOI":"10.3390\/v17111482","type":"journal-article","created":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T10:56:45Z","timestamp":1762513005000},"page":"1482","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Artificial Intelligence for Predicting Lung Immune Responses to Viral Infections: From Mechanistic Insights to Clinical Applications"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9162-7866","authenticated-orcid":false,"given":"Claudio","family":"Tana","sequence":"first","affiliation":[{"name":"Internal Medicine Unit, Eastern Hospital, ASL Taranto, 74024 Manduria, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Massimo","family":"Soloperto","sequence":"additional","affiliation":[{"name":"Internal Medicine Unit, Eastern Hospital, ASL Taranto, 74024 Manduria, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giampiero","family":"Giuliano","sequence":"additional","affiliation":[{"name":"Internal Medicine Unit, Eastern Hospital, ASL Taranto, 74024 Manduria, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giorgio","family":"Erroi","sequence":"additional","affiliation":[{"name":"Internal Medicine Unit, Eastern Hospital, ASL Taranto, 74024 Manduria, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antonio","family":"Di Maggio","sequence":"additional","affiliation":[{"name":"Internal Medicine Unit, Eastern Hospital, ASL Taranto, 74024 Manduria, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4504-9313","authenticated-orcid":false,"given":"Cosimo","family":"Tortorella","sequence":"additional","affiliation":[{"name":"Internal Medicine Unit, University Hospital of Taranto, 74123 Taranto, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Livia","family":"Moffa","sequence":"additional","affiliation":[{"name":"Infectious Disease Unit, University Hospital of Chieti, 66100 Chieti, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1361","DOI":"10.1016\/j.vaccine.2021.11.080","article-title":"Estimates of mortality associated with seasonal influenza for the European Union from the GLaMOR project","volume":"40","author":"Paget","year":"2022","journal-title":"Vaccine"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1111\/irv.12512","article-title":"Epidemiology and burden of influenza in the U.S. Department of Veterans Affairs","volume":"12","author":"Schirmer","year":"2018","journal-title":"Influenza Other Respir. Viruses"},{"key":"ref_3","unstructured":"GBD 2017 Influenza Collaborators (2019). Mortality, morbidity, and hospitalisations due to influenza lower respiratory tract infections, 2017: An analysis for the Global Burden of Disease Study 2017. Lancet Respir. Med., 7, 69\u201389."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bergeron, H.C., and Tripp, R.A. (2021). Immunopathology of RSV: An Updated Review. Viruses, 13.","DOI":"10.3390\/v13122478"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e13031","DOI":"10.1111\/irv.13031","article-title":"Respiratory syncytial virus disease burden in adults aged 60 years and older in high-income countries: A systematic literature review and meta-analysis","volume":"17","author":"Savic","year":"2023","journal-title":"Influenza Other Respir. Viruses"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Polverino, F., Stern, D.A., Ruocco, G., Balestro, E., Bassetti, M., Candelli, M., Cirillo, B., Contoli, M., Corsico, A., and D\u2019Amico, F. (2020). Comorbidities, Cardiovascular Therapies, and COVID-19 Mortality: A Nationwide, Italian Observational Study (ItaliCO). Front. Cardiovasc. Med., 7.","DOI":"10.3389\/fcvm.2020.585866"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"D\u2019Ambrosio, F., Lomazzi, M., Moore, M., Maida, A., Ricciardi, R., Munno, L., Lettieri, M., De Vito, E., Ricciardi, W., and Calabr\u00f2, G.E. (2025). Addressing the Underestimated Burden of RSV in Older Adults in Europe: Epidemiology, Surveillance Gaps, and Public Health Implications. Vaccines, 13.","DOI":"10.3390\/vaccines13050510"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Tana, C. (2024). Editorial: Frailty in older patients during the COVID-19 era. Front. Med., 10.","DOI":"10.3389\/fmed.2023.1348468"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.trsl.2021.09.001","article-title":"From ARDS to pulmonary fibrosis: The next phase of the COVID-19 pandemic?","volume":"241","author":"Michalski","year":"2022","journal-title":"Transl. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1007\/s10654-023-00973-x","article-title":"Prognostic models in COVID-19 infection that predict severity: A systematic review","volume":"38","author":"Buttia","year":"2023","journal-title":"Eur. J. Epidemiol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Moffa, L., and Tana, C. (2025). Healthcare-Associated Infections (HAI) in the Elderly: Molecular Mechanisms of Immunosenescence and Clinical, Nutritional and Therapeutic Implications. Int. J. Mol. Sci., 26.","DOI":"10.3390\/ijms26199649"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1189\/jlb.5MR0315-104R","article-title":"Paradoxical changes in innate immunity in aging: Recent progress and new directions","volume":"98","author":"Montgomery","year":"2015","journal-title":"J. Leukoc. Biol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1016\/j.humimm.2009.07.005","article-title":"Aging is associated with a numerical and functional decline in plasmacytoid dendritic cells, whereas myeloid dendritic cells are relatively unaltered in human peripheral blood","volume":"70","author":"Jing","year":"2009","journal-title":"Hum. Immunol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zheng, R., Zhang, Y., Zhang, K., Yuan, Y., Jia, S., and Liu, J. (2022). The Complement System, Aging, and Aging-Related Diseases. Int. J. Mol. Sci., 23.","DOI":"10.3390\/ijms23158689"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1038\/nri3307","article-title":"Transcriptional control of effector and memory CD8+ T cell differentiation","volume":"12","author":"Kaech","year":"2012","journal-title":"Nat. Rev. Immunol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1172\/JCI7558","article-title":"Analysis of the human thymic perivascular space during aging","volume":"104","author":"Flores","year":"1999","journal-title":"J. Clin. Investig."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Rocamora-Reverte, L., Melzer, F.L., W\u00fcrzner, R., and Weinberger, B. (2021). The Complex Role of Regulatory T Cells in Immunity and Aging. Front. Immunol., 11.","DOI":"10.3389\/fimmu.2020.616949"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Quiros-Roldan, E., Sottini, A., Natali, P.G., and Imberti, L. (2024). The Impact of Immune System Aging on Infectious Diseases. Microorganisms, 12.","DOI":"10.3390\/microorganisms12040775"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Sanz, I., Wei, C., Jenks, S.A., Cashman, K.S., Tipton, C., Woodruff, M.C., Hom, J., and Lee, F.E. (2019). Challenges and Opportunities for Consistent Classification of Human B Cell and Plasma Cell Populations. Front. Immunol., 10.","DOI":"10.3389\/fimmu.2019.02458"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"367","DOI":"10.14336\/AD.2018.0324","article-title":"Redefining Chronic Inflammation in Aging and Age-Related Diseases: Proposal of the Senoinflammation Concept","volume":"10","author":"Chung","year":"2019","journal-title":"Aging Dis."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/bs.ircmb.2018.05.006","article-title":"Mitochondria and Reactive Oxygen Species in Aging and Age-Related Diseases","volume":"340","author":"Giorgi","year":"2018","journal-title":"Int. Rev. Cell Mol. Biol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wiley, C.D., Schaum, N., Alimirah, F., Lopez-Dominguez, J.A., Orjalo, A.V., Scott, G., Desprez, P.Y., Benz, C., Davalos, A.R., and Campisi, J. (2018). Small-molecule MDM2 antagonists attenuate the senescence-associated secretory phenotype. Sci. Rep., 8.","DOI":"10.1038\/s41598-018-20000-4"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"L257","DOI":"10.1152\/ajplung.00238.2020","article-title":"Healing after COVID-19: Are survivors at risk for pulmonary fibrosis?","volume":"320","author":"McDonald","year":"2021","journal-title":"Am. J. Physiol. Lung Cell Mol. Physiol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1089\/jir.2021.0222","article-title":"Possible Role of Matrix Metalloproteinases and TGF-\u03b2 in COVID-19 Severity and Sequelae","volume":"42","year":"2022","journal-title":"J. Interferon Cytokine Res."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"L\u00f3pez-Gil, L., Pascual-Ahuir, A., and Proft, M. (2023). Genomic Instability and Epigenetic Changes during Aging. Int. J. Mol. Sci., 24.","DOI":"10.3390\/ijms241814279"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1080\/17476348.2020.1743181","article-title":"Artificial intelligence and machine learning in respiratory medicine","volume":"14","author":"Mekov","year":"2020","journal-title":"Expert. Rev. Respir. Med."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"eadp2407","DOI":"10.1126\/science.adp2407","article-title":"Disease diagnostics using machine learning of B cell and T cell receptor sequences","volume":"387","author":"Zaslavsky","year":"2025","journal-title":"Science."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tur, K. (2024). Multi-Modal Machine Learning Approach for COVID-19 Detection Using Biomarkers and X-Ray Imaging. Diagnostics, 14.","DOI":"10.3390\/diagnostics14242800"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Rahman, T., Chowdhury, M.E.H., Khandakar, A., Bin Mahbub, Z., Hossain, S.A., Alhatou, A., Abdalla, E., Muthiyal, S., Islam, K.F., and Kashem, S.B.A. (2022). BIO-CXRNET: A Robust Multimodal Stacking Machine Learning Technique for Mortality Risk Prediction of COVID-19 Patients using Chest X-Ray Images and Clinical Data. arXiv.","DOI":"10.1007\/s00521-023-08606-w"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"25655","DOI":"10.1073\/pnas.2011795117","article-title":"Immunological Genome Project Deep learning of immune cell differentiation","volume":"117","author":"Maslova","year":"2020","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1190","DOI":"10.1016\/j.cels.2024.11.008","article-title":"Predicting adaptive immune receptor specificities by machine learning is a data generation problem","volume":"15","author":"Mason","year":"2024","journal-title":"Cell Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.1093\/bioinformatics\/btt137","article-title":"PHISTO: Pathogen\u2013host interaction search tool","volume":"29","author":"Konuk","year":"2013","journal-title":"Bioinformatics"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"100551","DOI":"10.1016\/j.patter.2022.100551","article-title":"Accurate prediction of virus-host protein-protein interactions via a Siamese neural network using deep protein sequence embeddings","volume":"3","author":"Madan","year":"2022","journal-title":"Patterns"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1186\/s13073-024-01350-3","article-title":"Machine learning integrative approaches to advance computational immunology","volume":"16","author":"Curion","year":"2024","journal-title":"Genome Med."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Shiri, I., Mostafaei, S., Haddadi Avval, A., Salimi, Y., Sanaat, A., Akhavanallaf, A., Arabi, H., Rahmim, A., and Zaidi, H. (2022). High-dimensional multinomial multiclass severity scoring of COVID-19 pneumonia using CT radiomics features and machine learning algorithms. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-18994-z"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, H.J., Mao, L., Chen, Y., Yuan, L., Wang, F., Li, X., Cai, Q., Qiu, J., and Chen, F. (2021). Machine learning-based CT radiomics model distinguishes COVID-19 from non-COVID-19 pneumonia. BMC Infect. Dis., 21.","DOI":"10.1186\/s12879-021-06614-6"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hardy-Werbin, M., Maiques, J.M., Busto, M., Cirera, I., Aguirre, A., Garcia-Gisbert, N., Zuccarino, F., Carbullanca, S., Del Carpio, L.A., and Ramal, D. (2023). MultiCOVID: A multi modal deep learning approach for COVID-19 diagnosis. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-46126-8"},{"key":"ref_38","first-page":"S1473-3099(25)00354-8","article-title":"Artificial intelligence and infectious disease diagnostics: State of the art and future perspectives","volume":"16","author":"Miglietta","year":"2025","journal-title":"Lancet Infect. Dis."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Abbas, Q., Jeong, W., and Lee, S.W. (2025). Explainable AI in Clinical Decision Support Systems: A Meta-Analysis of Methods, Applications, and Usability Challenges. Healthcare, 13.","DOI":"10.3390\/healthcare13172154"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Lin, J., Gu, C., Sun, Z., Zhang, S., and Nie, S. (2024). Machine learning-based model for predicting theoccurrence and mortality of nonpulmonary sepsis-associated ARDS. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-79899-7"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"e340","DOI":"10.1016\/S2589-7500(21)00056-X","article-title":"Deep learning to detect acute respiratory distress syndrome on chest radiographs: A retrospective study with external validation","volume":"3","author":"Sjoding","year":"2021","journal-title":"Lancet Digit. Health"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Broecker, S., Adams, J.Y., Kumar, G., Callcut, R.A., Ni, Y., and Strohmer, T. (2025). Multimodal Deep Learning for ARDS Detection. medRxiv.","DOI":"10.1101\/2025.08.08.25333333"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ge, R., Wang, F., and Peng, Z. (2023). Advances in Biomarkers for Diagnosis and Treatment of ARDS. Diagnostics, 13.","DOI":"10.3390\/diagnostics13213296"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhou, K., Qin, L., Chen, Y., Gao, H., Ling, Y., Qin, Q., Mou, C., Qin, T., and Lu, J. (2025). A machine learning model for predicting acute respiratory distress syndrome risk in patients with sepsis using circulating immune cell parameters: A retrospective study. BMC Infect. Dis., 25.","DOI":"10.1186\/s12879-025-10974-8"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Tan, T.H., Hsu, C.C., Chen, C.J., Hsu, S.L., Liu, T.L., Lin, H.J., Wang, J.J., Liu, C.F., and Huang, C.C. (2021). Predicting outcomes in older ED patients with influenza in real time using a big data-driven and machine learning approach to the hospital information system. BMC Geriatr., 21.","DOI":"10.1186\/s12877-021-02229-3"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1111\/irv.12751","article-title":"Prediction of serious RSV-related outcomes in older adults with outpatient RSV respiratory illness during 12 consecutive seasons","volume":"14","author":"Kieke","year":"2020","journal-title":"Influenza Other Respir. Viruses"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Li, H., Ashrafi, N., Kang, C., Zhao, G., Chen, Y., and Pishgar, M. (2024). A machine learning-based prediction of hospital mortality in mechanically ventilated ICU patients. PLoS ONE, 19.","DOI":"10.1101\/2024.07.12.24310325"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Gao, J., Lu, Y., Ashrafi, N., Domingo, I., Alaei, K., and Pishgar, M. (2024). Prediction of sepsis mortality in ICU patients using machine learning methods. BMC Med. Inform. Decis. Mak., 24.","DOI":"10.1186\/s12911-024-02630-z"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Viderman, D., Ayazbay, A., Kalzhan, B., Bayakhmetova, S., Tungushpayev, M., and Abdildin, Y. (2024). Artificial Intelligence in the Management of Patients with Respiratory Failure Requiring Mechanical Ventilation: A Scoping Review. J. Clin. Med., 13.","DOI":"10.3390\/jcm13247535"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Bermejo-Pel\u00e1ez, D., San Jos\u00e9 Est\u00e9par, R., Fern\u00e1ndez-Velilla, M., Palacios Miras, C., Gallardo Madue\u00f1o, G., Benegas, M., Gotera Rivera, C., Cuerpo, S., Luengo-Oroz, M., and Sellar\u00e9s, J. (2022). Deep Learning-Based Lesion Subtyping and Prediction of Clinical Outcomes in COVID-19 Pneumonia Using Chest CT. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-13298-8"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Viderman, D., Abdildin, Y., Batkuldinova, K., Badenes, R., and Bilotta, F. (2023). Artificial Intelligence in Resuscitation: A Scoping Review. J. Clin. Med., 12.","DOI":"10.3390\/jcm12062254"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.1016\/j.bja.2025.02.024","article-title":"Weaning failure from mechanical ventilation: A scoping review of the utility of ultrasonography in the weaning process","volume":"135","author":"Gallardo","year":"2025","journal-title":"Br. J. Anaesth."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Xu, H., Ma, Y., Zhuang, Y., Zheng, Y., Du, Z., and Zhou, X. (2024). Machine learning-based risk prediction model construction of difficult weaning in ICU patients with mechanical ventilation. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-71548-3"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.1016\/j.immuni.2015.11.012","article-title":"Systems Analysis of Immunity to Influenza Vaccination across Multiple Years and in Diverse Populations Reveals Shared Molecular Signatures","volume":"43","author":"Nakaya","year":"2015","journal-title":"Immunity"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1080\/21645515.2019.1697110","article-title":"Methods for predicting vaccine immunogenicity and reactogenicity","volume":"16","author":"Lee","year":"2020","journal-title":"Hum. Vaccin. Immunother."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., and Kim, Y. (2021). Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-02353-5"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1109\/JBHI.2023.3328337","article-title":"AntiViralDL: Computational Antiviral Drug Repurposing Using Graph Neural Network and Self-Supervised Learning","volume":"28","author":"Zhang","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_58","first-page":"e77561","article-title":"Current Status and Future of Artificial Intelligence in Medicine","volume":"17","author":"Basubrin","year":"2025","journal-title":"Cureus"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Abu-El-Ruz, R., Hasan, A., Hijazi, D., Masoud, O., Abdallah, A.M., Zughaier, S.M., and Al-Asmakh, M. (2025). Artificial Intelligence in Biomedical Sciences: A Scoping Review. Br. J. Biomed. Sci., 82.","DOI":"10.3389\/bjbs.2025.14362"},{"key":"ref_60","unstructured":"Tana, C., Siniscalchi, C., Cerundolo, N., Meschi, T., Martelletti, P., Tana, M., Moffa, L., Wells-Gatnik, W., Cipollone, F., and Giamberardino, M.A. (2025). Smart Aging: Integrating AI into Elderly Healthcare. BMC Geriatr., in press."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1038\/s41746-024-01232-3","article-title":"Navigating the EU AI Act: Implications for regulated digital medical products","volume":"7","author":"Aboy","year":"2024","journal-title":"NPJ Digit. Med."}],"container-title":["Viruses"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4915\/17\/11\/1482\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T11:07:34Z","timestamp":1762513654000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4915\/17\/11\/1482"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,7]]},"references-count":61,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["v17111482"],"URL":"https:\/\/doi.org\/10.3390\/v17111482","relation":{},"ISSN":["1999-4915"],"issn-type":[{"value":"1999-4915","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,7]]}}}