{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T01:35:16Z","timestamp":1791941716179,"version":"4.3.4"},"reference-count":88,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T00:00:00Z","timestamp":1753833600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Pathogens"],"abstract":"<jats:p>Artificial intelligence (AI) techniques\u2014ranging from hybrid mechanistic\u2013machine learning (ML) ensembles to gradient-boosted decision trees, support-vector machines, and deep neural networks\u2014are transforming the management of seasonal influenza, respiratory syncytial virus (RSV), human immunodeficiency virus (HIV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Symptom-based triage models using eXtreme Gradient Boosting (XGBoost) and Random Forests, as well as imaging classifiers built on convolutional neural networks (CNNs), have improved diagnostic accuracy across respiratory infections. Transformer-based architectures and social media surveillance pipelines have enabled real-time monitoring of COVID-19. In HIV research, support-vector machines (SVMs), logistic regression, and deep neural network (DNN) frameworks advance viral-protein classification and drug-resistance mapping, accelerating antiviral and vaccine discovery. Despite these successes, persistent challenges remain\u2014data heterogeneity, limited model interpretability, hallucinations in large language models (LLMs), and infrastructure gaps in low-resource settings. We recommend standardized open-access data pipelines and integration of explainable-AI methodologies to ensure safe, equitable deployment of AI-driven interventions in future viral-outbreak responses.<\/jats:p>","DOI":"10.3390\/pathogens14080748","type":"journal-article","created":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T10:55:37Z","timestamp":1753872937000},"page":"748","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["AI Methods Tailored to Influenza, RSV, HIV, and SARS-CoV-2: A Focused Review"],"prefix":"10.3390","volume":"14","author":[{"given":"Achilleas","family":"Livieratos","sequence":"first","affiliation":[{"name":"Independent Researcher, 15238 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6983-2863","authenticated-orcid":false,"given":"George C.","family":"Kagadis","sequence":"additional","affiliation":[{"name":"Department of Medicine, University of Patras, 26504 Rio, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charalambos","family":"Gogos","sequence":"additional","affiliation":[{"name":"Department of Medicine, University of Patras, 26504 Rio, Greece"},{"name":"Metropolitan General Hospital, 15562 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4289-9494","authenticated-orcid":false,"given":"Karolina","family":"Akinosoglou","sequence":"additional","affiliation":[{"name":"Department of Medicine, University of Patras, 26504 Rio, Greece"},{"name":"Department of Internal Medicine and Infectious Diseases, University General Hospital of Patras, 26504 Rio, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Keshavarzi Arshadi, A., Webb, J., Salem, M., Cruz, E., Calad-Thomson, S., Ghadirian, N., Collins, J., Diez-Cecilia, E., Kelly, B., and Goodarzi, H. (2020). Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development. Front. Artif. Intell., 3.","DOI":"10.3389\/frai.2020.00065"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"860","DOI":"10.1093\/cid\/ciad633","article-title":"Black Box Warning: Large Language Models and the Future of Infectious Diseases Consultation","volume":"78","author":"Schwartz","year":"2024","journal-title":"Clin. Infect. Dis."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1177\/08465371231171125","article-title":"Accuracy of Information and References Using ChatGPT-3 for Retrieval of Clinical Radiological Information","volume":"75","author":"Wagner","year":"2024","journal-title":"Can. Assoc. Radiol. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e214","DOI":"10.1016\/S2589-7500(22)00029-2","article-title":"Explainability and artificial intelligence in medicine","volume":"4","author":"Reddy","year":"2022","journal-title":"Lancet Digit. Health"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"100954","DOI":"10.1016\/j.lanmic.2024.07.017","article-title":"The future of large language models in fighting emerging outbreaks: Lights and shadows","volume":"5","author":"Rizzo","year":"2024","journal-title":"Lancet Microbe"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"100881","DOI":"10.1016\/S2666-5247(24)00104-6","article-title":"Importance of investing time and money in integrating large language model-based agents into outbreak analytics pipelines","volume":"5","author":"Funk","year":"2024","journal-title":"Lancet Microbe."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"749","DOI":"10.4049\/jimmunol.1900033","article-title":"SIMON, an Automated Machine Learning System, Reveals Immune Signatures of Influenza Vaccine Responses","volume":"203","author":"Tomic","year":"2019","journal-title":"J. Immunol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hu, H., Wang, H., Wang, F., Langley, D., Avram, A., and Liu, M. (2018). Prediction of influenza-like illness based on the improved artificial tree algorithm and artificial neural network. Sci. Rep., 8.","DOI":"10.1038\/s41598-018-23075-1"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Reich, N.G., McGowan, C.J., Yamana, T.K., Tushar, A., Ray, E.L., Osthus, D., Kandula, S., Brooks, L.C., Crawford-Crudell, W., and Gibson, G.C. (2019). Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U. S. PLoS Comput. Biol., 15.","DOI":"10.1371\/journal.pcbi.1007486"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1896","DOI":"10.1080\/22221751.2021.1978824","article-title":"Influenza virus genotype to phenotype predictions through machine learning: A systematic review","volume":"10","author":"Borkenhagen","year":"2021","journal-title":"Emerg. Microbes Infect."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1038\/s41746-021-00511-7","article-title":"A prospective evaluation of AI-augmented epidemiology to forecast COVID-19 in the USA and Japan","volume":"4","author":"Shor","year":"2021","journal-title":"NPJ Digit. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"E65","DOI":"10.1148\/radiol.2020200905","article-title":"Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy","volume":"296","author":"Li","year":"2020","journal-title":"Radiology"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wolk, D.M., Lanyado, A., Tice, A.M., Shermohammed, M., Kinar, Y., Goren, A., Chabris, C.F., Meyer, M.N., Shoshan, A., and Abedi, V. (2022). Prediction of Influenza Complications: Development and Validation of a Machine Learning Prediction Model to Improve and Expand the Identification of Vaccine-Hesitant Patients at Risk of Severe Influenza Complications. J. Clin. Med., 11.","DOI":"10.3390\/jcm11154342"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Marquez, E., Barr\u00f3n-Palma, E.V., Rodr\u00edguez, K., Savage, J., and Sanchez-Sandoval, A.L. (2023). Supervised Machine Learning Methods for Seasonal Influenza Diagnosis. Diagnostics, 13.","DOI":"10.3390\/diagnostics13213352"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"363","DOI":"10.46234\/ccdcw2025.059","article-title":"Developing Machine Learning Models Based on Clinical Manifestations to Predict Influenza\u2014Chongqing Municipality, China, 2022\u20132023","volume":"7","author":"Zeng","year":"2025","journal-title":"China CDC Wkly."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hung, S.K., Wu, C.C., Singh, A., Li, J.H., Lee, C., Chou, E.H., Pekosz, A., Rothman, R., and Chen, K.F. (2023). Developing and validating clinical features-based machine learning algorithms to predict influenza infection in influenza-like illness patients. Biomed. J., 46.","DOI":"10.1016\/j.bj.2022.09.002"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Tian, Y., Zhang, W., Duan, L., McDonald, W., and Osgood, N. (2023). Comparison of pretrained transformer-based models for influenza and COVID-19 detection using social media text data in Saskatchewan, Canada. Front. Digit. Health, 5.","DOI":"10.3389\/fdgth.2023.1203874"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Baccega, D., Castagno, P., Fern\u00e1ndez Anta, A., and Sereno, M. (2024). Enhancing COVID-19 forecasting precision through the integration of compartmental models, machine learning and variants. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-69660-5"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Baik, S.M., Hong, K.S., and Park, D.J. (2023). Deep learning approach for early prediction of COVID-19 mortality using chest X-ray and electronic health records. BMC Bioinformatics., 24.","DOI":"10.1186\/s12859-023-05321-0"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Dipaola, F., Gatti, M., Giaj Levra, A., Men\u00e8, R., Shiffer, D., Faccincani, R., Raouf, Z., Secchi, A., Rovere Querini, P., and Voza, A. (2023). Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: A bi-centric study. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-37512-3"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5891177","DOI":"10.1155\/2024\/5891177","article-title":"Mortality prediction in COVID-19 using time series and machine learning techniques","volume":"2024","author":"Akter","year":"2024","journal-title":"Comput. Math. Methods Med."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zakariaee, S.S., Naderi, N., Ebrahimi, M., and Kazemi-Arpanahi, H. (2023). Comparing machine learning algorithms to predict COVID-19 mortality using a dataset including chest computed tomography severity score data. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-38133-6"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kawamoto, S., Morikawa, Y., and Yahagi, N. (2024). Novel Approach for Detecting Respiratory Syncytial Virus in Pediatric Patients Using Machine Learning Models Based on Patient-Reported Symptoms: Model Development and Validation Study. JMIR Form. Res., 8.","DOI":"10.2196\/52412"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Soriano-Arandes, A., Andr\u00e9s, C., Perramon-Malavez, A., Creus-Costa, A., Gatell, A., Mart\u00edn-Mart\u00edn, R., Sol\u00e0-Segura, E., Riera-Bosch, M.T., Fern\u00e1ndez, E., and Biosca, M. (2025). Implementing Symptom-Based Predictive Models for Early Diagnosis of Pediatric Respiratory Viral Infections. Viruses, 17.","DOI":"10.21203\/rs.3.rs-5490724\/v1"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Tso, C.F., Lam, C., Calvert, J., and Mao, Q. (2022). Machine learning early prediction of respiratory syncytial virus in pediatric hospitalized patients. Front. Pediatr., 10.","DOI":"10.3389\/fped.2022.886212"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Mei, J., and Zhao, J. (2018). Prediction of HIV-1 and HIV-2 proteins by using Chou\u2019s pseudo amino acid compositions and different classifiers. Sci. Rep., 8.","DOI":"10.1038\/s41598-018-20819-x"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1038\/s41592-024-02210-z","article-title":"Learning structural heterogeneity from cryo-electron sub-tomograms with tomoDRGN","volume":"21","author":"Powell","year":"2024","journal-title":"Nat. Methods"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1660","DOI":"10.1093\/bioinformatics\/bty842","article-title":"DeepHINT: Understanding HIV-1 integration via deep learning with attention","volume":"35","author":"Hu","year":"2019","journal-title":"Bioinformatics"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3002","DOI":"10.1097\/CM9.0000000000002932","article-title":"Machine learning identified genetic features associated with HIV sequences in the monocytes","volume":"136","author":"Peng","year":"2023","journal-title":"Chin. Med. J."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chen, X., Wang, Z.X., and Pan, X.M. (2019). HIV-1 tropism prediction by the XGboost and HMM methods. Sci. Rep., 9.","DOI":"10.1038\/s41598-019-46420-4"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Roche, S.D., Ekwunife, O.I., Mendonca, R., Kwach, B., Omollo, V., Zhang, S., Ongwen, P., Hattery, D., Smedinghoff, S., and Morris, S. (2024). Measuring the performance of computer vision artificial intelligence to interpret images of HIV self-testing results. Front. Public Health, 12.","DOI":"10.3389\/fpubh.2024.1334881"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1038\/s41591-021-01384-9","article-title":"Deep learning of HIV field-based rapid tests","volume":"27","author":"Herbst","year":"2021","journal-title":"Nat. Med."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Benitez, A.E., Musinguzi, N., Bangsberg, D.R., Bwana, M.B., Muzoora, C., Hunt, P.W., Martin, J.N., Haberer, J.E., and Petersen, M.L. (2020). Super learner analysis of real-time electronically monitored adherence to antiretroviral therapy under constrained optimization and comparison to non-differentiated care approaches for persons living with HIV in rural Uganda. J. Int. AIDS Soc., 23.","DOI":"10.1002\/jia2.25467"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2326","DOI":"10.1093\/cid\/ciz1096","article-title":"Machine Learning to Identify Persons at High-Risk of Human Immunodeficiency Virus Acquisition in Rural Kenya and Uganda","volume":"71","author":"Balzer","year":"2020","journal-title":"Clin. Infect. Dis."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.jinf.2020.11.007","article-title":"Predicting the diagnosis of HIV and sexually transmitted infections among men who have sex with men using machine learning approaches","volume":"82","author":"Bao","year":"2021","journal-title":"J. Infect."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wei, Y., Li, W., Du, T., Hong, Z., and Lin, J. (2019). Targeting HIV\/HCV Coinfection Using a Machine Learning-Based Multiple Quantitative Structure-Activity Relationships (Multiple QSAR) Method. Int. J. Mol. Sci., 20.","DOI":"10.1101\/605162"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, Y., Li, Y., Chen, X., and Zhao, L. (2023). HIV-1\/HBV Coinfection Accurate Multitarget Prediction Using a Graph Neural Network-Based Ensemble Predicting Model. Int. J. Mol. Sci., 24.","DOI":"10.3390\/ijms24087139"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Onywera, H., Williamson, A.L., Cozzuto, L., Bonnin, S., Mbulawa, Z.Z.A., Coetzee, D., Ponomarenko, J., and Meiring, T.L. (2020). The penile microbiota of Black South African men: Relationship with human papillomavirus and HIV infection. BMC Microbiol., 20.","DOI":"10.1186\/s12866-020-01759-x"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Namalinzi, F., Galadima, K.R., Nalwanga, R., Sekitoleko, I., and Uwimbabazi, L.F.R. (2024). Prediction of precancerous cervical cancer lesions among women living with HIV on antiretroviral therapy in Uganda: A comparison of supervised machine learning algorithms. BMC Womens Health, 24.","DOI":"10.1186\/s12905-024-03232-7"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Arrigoni, R., Santacroce, L., Ballini, A., and Palese, L.L. (2023). AI-Aided Search for New HIV-1 Protease Ligands. Biomolecules, 13.","DOI":"10.3390\/biom13050858"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3679","DOI":"10.1021\/acs.jcim.9b00457","article-title":"Target-Specific Prediction of Ligand Affinity with Structure-Based Interaction Fingerprints","volume":"59","author":"Leidner","year":"2019","journal-title":"J. Chem. Inf. Model."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1002\/psp4.13085","article-title":"Computational drug discovery on human immunodeficiency virus with a customized long short-term memory variational autoencoder deep-learning architecture","volume":"13","author":"Kutsal","year":"2024","journal-title":"CPT Pharmacomet. Syst. Pharmacol."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Pham, T., Ghafoor, M., Gra\u00f1ana-Castillo, S., Marzolini, C., Gibbons, S., Khoo, S., Chiong, J., Wang, D., and Siccardi, M. (2024). DeepARV: Ensemble deep learning to predict drug-drug interaction of clinical relevance with antiretroviral therapy. NPJ Syst. Biol. Appl., 10.","DOI":"10.1038\/s41540-024-00374-0"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Rawi, R., Mall, R., Shen, C.H., Farney, S.K., Shiakolas, A., Zhou, J., Bensmail, H., Chun, T.W., Doria-Rose, N.A., and Lynch, R.M. (2019). Accurate Prediction for Antibody Resistance of Clinical HIV-1 Isolates. Sci. Rep., 9.","DOI":"10.1038\/s41598-019-50635-w"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Steiner, M.C., Gibson, K.M., and Crandall, K.A. (2020). Drug Resistance Prediction Using Deep Learning Techniques on HIV-1 Sequence Data. Viruses., 12.","DOI":"10.3390\/v12050560"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1541","DOI":"10.1007\/s11030-021-10262-y","article-title":"Predicting HIV drug resistance using weighted machine learning method at target protein sequence-level","volume":"25","author":"Cai","year":"2021","journal-title":"Mol Divers."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Blassel, L., Tostevin, A., Villabona-Arenas, C.J., Peeters, M., Hu\u00e9, S., and Gascuel, O. (2021). UK HIV Drug Resistance Database. Using machine learning and big data to explore the drug resistance landscape in HIV. PLoS Comput. Biol., 17.","DOI":"10.1101\/2021.03.16.435621"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Altamirano-Flores, J.S., Alvarado-Hern\u00e1ndez, L.\u00c1., Cuevas-Tello, J.C., Tino, P., Guerra-Palomares, S.E., and Garcia-Sepulveda, C.A. (2023). Identification of Clinically Relevant HIV Vif Protein Motif Mutations through Machine Learning and Undersampling. Cells, 12.","DOI":"10.3390\/cells12050772"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Li, J., Hao, Y., Liu, Y., Wu, L., Liang, H., Ni, L., Wang, F., Wang, S., Duan, Y., and Xu, Q. (2024). Supervised machine learning algorithms to predict the duration and risk of long-term hospitalization in HIV-infected individuals: A retrospective study. Front. Public Health, 11.","DOI":"10.3389\/fpubh.2023.1282324"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Semenova, L., Wang, Y., Falcinelli, S., Archin, N., Cooper-Volkheimer, A.D., Margolis, D.M., Goonetilleke, N., Murdoch, D.M., Rudin, C.D., and Browne, E.P. (2024). Machine learning approaches identify immunologic signatures of total and intact HIV DNA during long-term antiretroviral therapy. eLife, 13.","DOI":"10.7554\/eLife.94899"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1186\/s12977-018-0401-x","article-title":"Prediction of HIV-associated neurocognitive disorder (HAND) from three genetic features of envelope gp120 glycoprotein","volume":"15","author":"Ogishi","year":"2018","journal-title":"Retrovirology"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1785","DOI":"10.1097\/QAD.0000000000002955","article-title":"Predictive variables for peripheral neuropathy in treated HIV type 1 infection revealed by machine learning","volume":"35","author":"Tu","year":"2021","journal-title":"AIDS"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1097\/QAD.0000000000002471","article-title":"Machine-learning classification of neurocognitive performance in children with perinatal HIV initiating de novo antiretroviral therapy","volume":"34","author":"Paul","year":"2020","journal-title":"AIDS"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1093\/infdis\/jiac156","article-title":"Machine Learning Quantifies Accelerated White-Matter Aging in Persons With HIV","volume":"226","author":"Petersen","year":"2022","journal-title":"J. Infect. Dis."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1186\/s13148-018-0591-z","article-title":"Machine learning selected smoking-associated DNA methylation signatures that predict HIV prognosis and mortality","volume":"10","author":"Zhang","year":"2018","journal-title":"Clin. Epigenetics."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"McGowan, E., Rosenthal, R., Fiore-Gartland, A., Macharia, G., Balinda, S., Kapaata, A., Umviligihozo, G., Muok, E., Dalel, J., and Streatfield, C.L. (2021). Utilizing Computational Machine Learning Tools to Understand Immunogenic Breadth in the Context of a CD8 T-Cell Mediated HIV Response. Front. Immunol., 12.","DOI":"10.3389\/fimmu.2021.609884"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"4278","DOI":"10.1093\/bioinformatics\/btac530","article-title":"Prediction of HIV sensitivity to monoclonal antibodies using aminoacid sequences and deep learning","volume":"38","author":"Buiu","year":"2022","journal-title":"Bioinformatics"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1186\/s12967-024-05147-1","article-title":"Predicting humoral responses to primary and booster SARS-CoV-2 mRNA vaccination in people living with HIV: A machine learning approach","volume":"22","author":"Montesi","year":"2024","journal-title":"J. Transl. Med."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Jin, R., and Zhang, L. (2025). AI applications in HIV research: Advances and future directions. Front. Microbiol., 16.","DOI":"10.3389\/fmicb.2025.1541942"},{"key":"ref_60","unstructured":"Centers for Disease Control and Prevention (2025, April 24). Data Modernization Initiative: Technologies\u2014Artificial Intelligence and Machine Learning. Atlanta (GA): CDC, Available online: https:\/\/www.cdc.gov\/surveillance\/data-modernization\/technologies\/ai-ml.html."},{"key":"ref_61","unstructured":"European Centre for Disease Prevention and Control (2025, April 24). Digital Technologies for Surveillance, Prevention and Control of Infectious Diseases: A Scoping Review. Stockholm: ECDC. Available online: https:\/\/www.ecdc.europa.eu\/en\/publications-data\/digital-technologies-surveillance-prevention-and-control-infectious-diseases."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1038\/s43856-025-00790-1","article-title":"Transformers and large language models are efficient feature extractors for electronic health record studies","volume":"5","author":"Yuan","year":"2025","journal-title":"Commun. Med."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1038\/s41586-023-06291-2","article-title":"Large language models encode clinical knowledge","volume":"620","author":"Singhal","year":"2023","journal-title":"Nature"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1038\/s41586-023-06160-y","article-title":"Health system-scale language models are all-purpose prediction engines","volume":"619","author":"Jiang","year":"2023","journal-title":"Nature"},{"key":"ref_65","first-page":"9","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"Radford","year":"2019","journal-title":"OpenAI Blog"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/s13755-021-00158-4","article-title":"Monitoring COVID-19 pandemic through the lens of social media using natural language processing and machine learning","volume":"9","author":"Liu","year":"2021","journal-title":"Health Inf. Sci. Syst."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"796.e1","DOI":"10.1016\/j.jvoice.2021.11.004","article-title":"Machine Learning-based Voice Assessment for the Detection of Positive and Recovered COVID-19 Patients","volume":"38","author":"Robotti","year":"2024","journal-title":"J. Voice"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"3165","DOI":"10.1038\/s41467-025-58527-6","article-title":"Machine learning in point-of-care testing: Innovations, challenges, and opportunities","volume":"16","author":"Han","year":"2025","journal-title":"Nat. Commun."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1268","DOI":"10.1007\/s44197-024-00284-8","article-title":"Global Research on Pandemics or Epidemics and Mental Health: A Natural Language Processing Study","volume":"14","author":"Ye","year":"2024","journal-title":"J. Epidemiol. Glob. Health"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Islam, N., Mohsin, A.S.M., Choudhury, S.H., Shaer, T.P., Islam, M.A., Sadat, O., and Taz, N.H. (2024). COVID-19 and Pneumonia detection and web deployment from CT scan and X-ray images using deep learning. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0302413"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"32755","DOI":"10.1007\/s11042-023-14642-4","article-title":"Artificial intelligence centric scientific research on COVID-19: An analysis based on scientometrics data","volume":"82","author":"Shukla","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Mahalakshmi, V., Balobaid, A., Kanisha, B., Sasirekha, R., and Ramkumar, R.M. (2023). Artificial Intelligence: A Next-Level Approach in Confronting the COVID-19 Pandemic. Healthcare, 11.","DOI":"10.3390\/healthcare11060854"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"21471","DOI":"10.1007\/s11042-022-11913-4","article-title":"Perspective of AI system for COVID-19 detection using chest images: A review","volume":"81","author":"Das","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/rmv.2205","article-title":"How artificial intelligence may help the Covid-19 pandemic: Pitfalls and lessons for the future","volume":"31","author":"Malik","year":"2021","journal-title":"Rev. Med. Virol."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Kim, T.H., Chinthaginjala, R., Srinivasulu, A., Tera, S.P., and Rab, S.O. (2025). COVID-19 health data prediction: A critical evaluation of CNN-based approaches. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-92464-0"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Garrido, N.J., Gonz\u00e1lez-Mart\u00ednez, F., Torres, A.M., Blasco-Segura, P., Losada, S., Plaza, A., and Mateo, J. (2025). Role of Artificial Intelligence in Identifying Vital Biomarkers with Greater Precision in Emergency Departments During Emerging Pandemics. Int. J. Mol. Sci., 26.","DOI":"10.3390\/ijms26020722"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"002067","DOI":"10.1099\/jgv.0.002067","article-title":"Unveiling the ghost: Machine learning\u2019s impact on the landscape of virology","volume":"106","author":"Bowyer","year":"2025","journal-title":"J. Gen. Virol."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1080\/03091902.2024.2321846","article-title":"Artificial intelligence and machine learning responses to COVID-19 related inquiries","volume":"47","author":"Zaeri","year":"2023","journal-title":"J. Med. Eng. Technol."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1370","DOI":"10.1016\/j.csbj.2025.03.044","article-title":"Predicting pathogen evolution and immune evasion in the age of artificial intelligence","volume":"27","author":"Hamelin","year":"2025","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.csbj.2024.05.014","article-title":"Using artificial intelligence and predictive modelling to enable learning healthcare systems (LHS) for pandemic preparedness","volume":"24","author":"Ankolekar","year":"2024","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1859","DOI":"10.3233\/THC-230917","article-title":"Application of artificial intelligence for the classification of the clinical outcome and therapy in patients with viral infections: The case of COVID-19","volume":"32","author":"Bego","year":"2024","journal-title":"Technol. Health Care"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1038\/s41586-024-08564-w","article-title":"Artificial intelligence for modelling infectious disease epidemics","volume":"638","author":"Kraemer","year":"2025","journal-title":"Nature"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/978-1-0716-4326-6_15","article-title":"Artificial Intelligence Methods in Infection Biology Research","volume":"2890","author":"Anter","year":"2025","journal-title":"Methods Mol. Biol."},{"key":"#cr-split#-ref_84.1","unstructured":"European Union (2016). Regulation"},{"key":"#cr-split#-ref_84.2","unstructured":"(EU) 2016\/679 of the European Parliament and of the Council of 27 April 2016 (General Data Protection Regulation). Off. J. Eur. Union, L119, 1-88. Available online: https:\/\/gdpr-info.eu\/."},{"key":"ref_85","unstructured":"Centers for Disease Control and Prevention (2025, July 22). Health Insurance Portability and Accountability Act of 1996 (HIPAA). Public Health Law Program, Available online: https:\/\/www.cdc.gov\/phlp\/php\/resources\/health-insurance-portability-and-accountability-act-of-1996-hipaa.html."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Cross, J.L., Choma, M.A., and Onofrey, J.A. (2024). Bias in medical AI: Implications for clinical decision-making. PLoS Digit. Health, 3.","DOI":"10.1371\/journal.pdig.0000651"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Arvai, N., Katonai, G., and Mesko, B. (2025). Health Care Professionals\u2019 Concerns About Medical AI and Psychological Barriers and Strategies for Successful Implementation: Scoping Review. J. Med. Internet Res., 27.","DOI":"10.2196\/66986"}],"container-title":["Pathogens"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-0817\/14\/8\/748\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:18:52Z","timestamp":1760033932000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-0817\/14\/8\/748"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,30]]},"references-count":88,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["pathogens14080748"],"URL":"https:\/\/doi.org\/10.3390\/pathogens14080748","relation":{},"ISSN":["2076-0817"],"issn-type":[{"value":"2076-0817","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,30]]}}}