{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T15:00:12Z","timestamp":1788447612960,"version":"build-2803163510"},"reference-count":81,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T00:00:00Z","timestamp":1757980800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Head and neck cancer (HNC) encompasses malignant neoplasms originating from the soft tissues of the nasal cavity, paranasal sinuses, oral cavity, pharynx, larynx, skin, and thyroid [...]<\/jats:p>","DOI":"10.3390\/cancers17183023","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T11:19:31Z","timestamp":1758021571000},"page":"3023","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Artificial Intelligence in Head and Neck Cancer: Towards Precision Medicine"],"prefix":"10.3390","volume":"17","author":[{"given":"Jacob","family":"Hagen","sequence":"first","affiliation":[{"name":"Department of Otolaryngology-Head and Neck Surgery, Louisiana State University Health Sciences Center, New Orleans, LA 70112, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Logan","family":"Hornung","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology-Head and Neck Surgery, Louisiana State University Health Sciences Center, New Orleans, LA 70112, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William","family":"Barham","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology-Head and Neck Surgery, Louisiana State University Health Sciences Center, New Orleans, LA 70112, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Supratik","family":"Mukhopadhyay","sequence":"additional","affiliation":[{"name":"Department of Environmental Sciences, Center for Computation & Technology, Louisiana State University, Baton Rouge, LA 70803, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adam","family":"Bess","sequence":"additional","affiliation":[{"name":"Department of Environmental Sciences, Center for Computation & Technology, Louisiana State University, Baton Rouge, LA 70803, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin","family":"Contrera","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology-Head and Neck Surgery, University of Pittsburgh, Pittsburgh, PA 15260, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Devraj","family":"Basu","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology-Head and Neck Surgery, University of Pennsylvania, Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9205-385X","authenticated-orcid":false,"given":"Vlad","family":"Sandulache","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology-Head and Neck Surgery, Baylor College of Medicine, Houston, TX 77030, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guillaume","family":"Spielmann","sequence":"additional","affiliation":[{"name":"School of Kinesiology, Louisiana State University, Baton Rouge, LA 70803, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3431-5986","authenticated-orcid":false,"given":"Sagar","family":"Kansara","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology-Head and Neck Surgery, Louisiana State University Health Sciences Center, New Orleans, LA 70112, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,16]]},"reference":[{"key":"ref_1","unstructured":"(2025, March 30). SEER*Explorer Application, Available online: https:\/\/seer.cancer.gov\/statistics-network\/explorer\/application.html?site=80&data_type=1&graph_type=2&compareBy=sex&chk_sex_1=1&chk_sex_3=3&chk_sex_2=2&rate_type=2&race=1&age_range=1&stage=101&advopt_precision=1&advopt_show_ci=on&hdn_view=0&advopt_show_apc=on&advopt_display=1#resultsRegion0."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Barsouk, A., Aluru, J.S., Rawla, P., Saginala, K., and Barsouk, A. (2023). Epidemiology, Risk Factors, and Prevention of Head and Neck Squamous Cell Carcinoma. Med. Sci., 11.","DOI":"10.3390\/medsci11020042"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1016\/j.oraloncology.2013.12.019","article-title":"Epidemiology of HPV-Associated Oropharyngeal Cancer","volume":"50","author":"Pytynia","year":"2014","journal-title":"Oral Oncol."},{"key":"ref_4","first-page":"14","article-title":"Introduction to Machine Learning, Neural Networks, and Deep Learning","volume":"9","author":"Choi","year":"2020","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5255","DOI":"10.3390\/curroncol31090389","article-title":"Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions","volume":"31","author":"Pham","year":"2024","journal-title":"Curr. Oncol. Tor. Ont"},{"key":"ref_6","unstructured":"Muneer, A., Waqas, M., Saad, M.B., Showkatian, E., Bandyopadhyay, R., Xu, H., Li, W., Chang, J.Y., Liao, Z., and Haymaker, C. (2025). From Classical Machine Learning to Emerging Foundation Models: Review on Multimodal Data Integration for Cancer Research. arXiv."},{"key":"ref_7","unstructured":"Klontzas, M.E., Fanni, S.C., Febi, M., Aghakhanyan, G., and Neri, E. (2023). Introduction to Artificial Intelligence, Springer International Publishing AG. [1st ed.]."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chowdhary, K.R. (2020). Fundamentals of Artificial Intelligence, Springer.","DOI":"10.1007\/978-81-322-3972-7"},{"key":"ref_9","first-page":"44","article-title":"A Systematic Review of Natural Language Processing in Healthcare","volume":"7","author":"Iroju","year":"2015","journal-title":"Int. J. Inf. Technol. Comput. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ma, L., Lu, G., Wang, D., Qin, X., Chen, Z.G., and Fei, B. (2019). Adaptive Deep Learning for Head and Neck Cancer Detection Using Hyperspectral Imaging. Vis. Comput. Ind. Biomed. Art, 2.","DOI":"10.1186\/s42492-019-0023-8"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Stephens, H., Li, X., Sheng, Y., Wu, Q., Ge, Y., and Wu, Q.J. (2024). A Reinforcement Learning Agent for Head and Neck Intensity-Modulated Radiation Therapy. Front. Phys., 12.","DOI":"10.3389\/fphy.2024.1331849"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Meneghetti, A.R., Hern\u00e1ndez, M.L., Kuehn, J.-P., L\u00f6ck, S., Carrero, Z.I., Perez-Lopez, R., Bressem, K., Brinker, T.K., Pearson, A.T., and Truhn, D. (MedRxiv Serv. Health Sci., 2025). End-to-End Prediction of Clinical Outcomes in Head and Neck Squamous Cell Carcinoma with Foundation Model-Based Multiple Instance Learning, MedRxiv Serv. Health Sci., preprint.","DOI":"10.1186\/s44398-025-00003-8"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Illimoottil, M., and Ginat, D. (2023). Recent Advances in Deep Learning and Medical Imaging for Head and Neck Cancer Treatment: MRI, CT, and PET Scans. Cancers, 15.","DOI":"10.3390\/cancers15133267"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"217","DOI":"10.5603\/EP.a2021.0015","article-title":"A Comparison between Deep Learning Convolutional Neural Networks and Radiologists in the Differentiation of Benign and Malignant Thyroid Nodules on CT Images","volume":"72","author":"Zhao","year":"2021","journal-title":"Endokrynol. Pol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"271","DOI":"10.21873\/anticanres.13949","article-title":"Prediction of Tumor Grade and Nodal Status in Oropharyngeal and Oral Cavity Squamous-Cell Carcinoma Using a Radiomic Approach","volume":"40","author":"Romeo","year":"2020","journal-title":"Anticancer Res."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kann, B.H., Aneja, S., Loganadane, G.V., Kelly, J.R., Smith, S.M., Decker, R.H., Yu, J.B., Park, H.S., Yarbrough, W.G., and Malhotra, A. (2018). Pretreatment Identification of Head and Neck Cancer Nodal Metastasis and Extranodal Extension Using Deep Learning Neural Networks. Sci. Rep., 8.","DOI":"10.1038\/s41598-018-32441-y"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"401","DOI":"10.2463\/mrms.rev.2023-0047","article-title":"Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging","volume":"22","author":"Fujima","year":"2023","journal-title":"Magn. Reson. Med. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"272","DOI":"10.2463\/mrms.mp.2020-0082","article-title":"MR Imaging of Endolymphatic Hydrops: Utility of iHYDROPS-Mi2 Combined with Deep Learning Reconstruction Denoising","volume":"20","author":"Naganawa","year":"2020","journal-title":"Magn. Reson. Med. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1007\/s11604-025-01756-y","article-title":"Dual-Type Deep Learning-Based Image Reconstruction for Advanced Denoising and Super-Resolution Processing in Head and Neck T2-Weighted Imaging","volume":"43","author":"Fujima","year":"2025","journal-title":"Jpn. J. Radiol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1053\/j.semnuclmed.2025.03.003","article-title":"Artificial Intelligence for Tumor [18F] FDG-PET Imaging: Advancement and Future Trends\u2014Part I","volume":"55","author":"Safarian","year":"2025","journal-title":"Semin. Nucl. Med."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Haider, S.P., Zeevi, T., Baumeister, P., Reichel, C., Sharaf, K., Forghani, R., Kann, B.H., Judson, B.L., Prasad, M.L., and Burtness, B. (2020). Potential Added Value of PET\/CT Radiomics for Survival Prognostication beyond AJCC 8th Edition Staging in Oropharyngeal Squamous Cell Carcinoma. Cancers, 12.","DOI":"10.3390\/cancers12071778"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1007\/s00234-024-03485-x","article-title":"Diagnostic Accuracy of Radiomics and Artificial Intelligence Models in Diagnosing Lymph Node Metastasis in Head and Neck Cancers: A Systematic Review and Meta-Analysis","volume":"67","author":"Valizadeh","year":"2025","journal-title":"Neuroradiology"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e9","DOI":"10.1016\/j.crad.2020.08.030","article-title":"Machine Learning-Based FDG PET-CT Radiomics for Outcome Prediction in Larynx and Hypopharynx Squamous Cell Carcinoma","volume":"76","author":"Zhong","year":"2021","journal-title":"Clin. Radiol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5980","DOI":"10.1364\/BOE.527972","article-title":"Machine Learning for Automated Classification of Lung Collagen in a Urethane-Induced Lung Injury Mouse Model","volume":"15","author":"Alnafisah","year":"2024","journal-title":"Biomed. Opt. Express"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1364\/BOE.410347","article-title":"Characterization of Fibrillar Collagen Isoforms in Infarcted Mouse Hearts Using Second Harmonic Generation Imaging","volume":"12","author":"Sahu","year":"2021","journal-title":"Biomed. Opt. Express"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ahmed, A.A., Abouzid, M., and Kaczmarek, E. (2022). Deep Learning Approaches in Histopathology. Cancers, 14.","DOI":"10.3390\/cancers14215264"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4876","DOI":"10.7150\/jca.28769","article-title":"Deep Convolutional Neural Network VGG-16 Model for Differential Diagnosing of Papillary Thyroid Carcinomas in Cytological Images: A Pilot Study","volume":"10","author":"Guan","year":"2019","journal-title":"J. Cancer"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"468","DOI":"10.21037\/atm.2019.08.54","article-title":"Using Deep Convolutional Neural Networks for Multi-Classification of Thyroid Tumor by Histopathology: A Large-Scale Pilot Study","volume":"7","author":"Wang","year":"2019","journal-title":"Ann. Transl. Med."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Fati, S.M., Senan, E.M., and Javed, Y. (2022). Early Diagnosis of Oral Squamous Cell Carcinoma Based on Histopathological Images Using Deep and Hybrid Learning Approaches. Diagnostics, 12.","DOI":"10.3390\/diagnostics12081899"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"85","DOI":"10.21053\/ceo.2023.00026","article-title":"Development and Validation of a Pathomics Model Using Machine Learning to Predict CXCL8 Expression and Prognosis in Head and Neck Cancer","volume":"17","author":"Wang","year":"2024","journal-title":"Clin. Exp. Otorhinolaryngol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e18394","DOI":"10.1111\/jcmm.18394","article-title":"Prognostic Value of CDKN2A in Head and Neck Squamous Cell Carcinoma via Pathomics and Machine Learning","volume":"28","author":"Wang","year":"2024","journal-title":"J. Cell. Mol. Med."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"6064","DOI":"10.1200\/JCO.2024.42.16_suppl.6064","article-title":"Multi-Feature next-Generation Liquid Biopsy for Diagnosis and Prognosis in HPV-Associated Head and Neck Cancer","volume":"42","author":"Aye","year":"2024","journal-title":"J. Clin. Oncol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Das, D., Hirayama, S., Aye, L., Bryan, M.E., Naegele, S., Zhao, B., Efthymiou, V., Mendel, J., Fisch, A.S., and Kr\u00f6ller, L. (2024). Blood-Based Screening for HPV-Associated Cancers. medRxiv, preprint.","DOI":"10.1101\/2024.01.04.24300841"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/jrr\/rrad090","article-title":"Revolutionizing Radiation Therapy: The Role of AI in Clinical Practice","volume":"65","author":"Kawamura","year":"2024","journal-title":"J. Radiat. Res."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lucido, J.J., DeWees, T.A., Leavitt, T.R., Anand, A., Beltran, C.J., Brooke, M.D., Buroker, J.R., Foote, R.L., Foss, O.R., and Gleason, A.M. (2023). Validation of Clinical Acceptability of Deep-Learning-Based Automated Segmentation of Organs-at-Risk for Head-and-Neck Radiotherapy Treatment Planning. Front. Oncol., 13.","DOI":"10.3389\/fonc.2023.1137803"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1002\/mrm.28738","article-title":"Super-Resolution Head and Neck MRA Using Deep Machine Learning","volume":"86","author":"Koktzoglou","year":"2021","journal-title":"Magn. Reson. Med."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Pertzborn, D., Nguyen, H.-N., H\u00fcttmann, K., Prengel, J., Ernst, G., Guntinas-Lichius, O., von Eggeling, F., and Hoffmann, F. (2022). Intraoperative Assessment of Tumor Margins in Tissue Sections with Hyperspectral Imaging and Machine Learning. Cancers, 15.","DOI":"10.3390\/cancers15010213"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Halicek, M., Dormer, J.D., Little, J.V., Chen, A.Y., Myers, L., Sumer, B.D., and Fei, B. (2019). Hyperspectral Imaging of Head and Neck Squamous Cell Carcinoma for Cancer Margin Detection in Surgical Specimens from 102 Patients Using Deep Learning. Cancers, 11.","DOI":"10.3390\/cancers11091367"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5426","DOI":"10.1158\/1078-0432.CCR-17-0906","article-title":"Detection of Head and Neck Cancer in Surgical Specimens Using Quantitative Hyperspectral Imaging","volume":"23","author":"Lu","year":"2017","journal-title":"Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1007\/s11701-025-02555-3","article-title":"AI-Driven Robotic Surgery in Oncology: Advancing Precision, Personalization, and Patient Outcomes","volume":"19","year":"2025","journal-title":"J. Robot. Surg."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Zhang, Y., Lin, L., Huang, W., Xiao, C., Liu, J., and Chai, G. (2024). Intelligent Electromagnetic Navigation System for Robot-Assisted Intraoral Osteotomy in Mandibular Tumor Resection: A Model Experiment. Front. Immunol., 15.","DOI":"10.3389\/fimmu.2024.1436276"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"7412","DOI":"10.1007\/s00464-023-10335-z","article-title":"Technical Skill Assessment in Minimally Invasive Surgery Using Artificial Intelligence: A Systematic Review","volume":"37","author":"Pedrett","year":"2023","journal-title":"Surg. Endosc."},{"key":"ref_43","unstructured":"(2025, April 26). Home-GEO-NCBI, Available online: https:\/\/www.ncbi.nlm.nih.gov\/geo\/."},{"key":"ref_44","unstructured":"(2025, April 26). The Cancer Genome Atlas Program (TCGA)\u2014NCI, Available online: https:\/\/www.cancer.gov\/ccg\/research\/genome-sequencing\/tcga."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.1007\/s11030-021-10217-3","article-title":"Artificial Intelligence to Deep Learning: Machine Intelligence Approach for Drug Discovery","volume":"25","author":"Gupta","year":"2021","journal-title":"Mol. Divers."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.drudis.2020.10.010","article-title":"Artificial Intelligence in Drug Discovery and Development","volume":"26","author":"Paul","year":"2021","journal-title":"Drug Discov. Today"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1016\/j.drudis.2021.10.022","article-title":"Artificial Intelligence for the Discovery of Novel Antimicrobial Agents for Emerging Infectious Diseases","volume":"27","author":"Bess","year":"2022","journal-title":"Drug Discov. Today"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Pu, L., Naderi, M., Liu, T., Wu, H.-C., Mukhopadhyay, S., and Brylinski, M. (2019). eToxPred: A Machine Learning-Based Approach to Estimate the Toxicity of Drug Candidates. BMC Pharmacol. Toxicol., 20.","DOI":"10.1186\/s40360-018-0282-6"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1021\/acs.jcim.6b00596","article-title":"Break Down in Order To Build Up: Decomposing Small Molecules for Fragment-Based Drug Design with eMolFrag","volume":"57","author":"Liu","year":"2017","journal-title":"J. Chem. Inf. Model."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1186\/s13321-016-0126-6","article-title":"A Graph-Based Approach to Construct Target-Focused Libraries for Virtual Screening","volume":"8","author":"Naderi","year":"2016","journal-title":"J. Cheminform."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Bess, A., Berglind, F., Mukhopadhyay, S., Brylinski, M., Alvin, C., Fattah, F., and Wasan, K.M. (2023). Identification of Oral Therapeutics Using an AI Platform against the Virus Responsible for COVID-19, SARS-CoV-2. Front. Pharmacol., 14.","DOI":"10.3389\/fphar.2023.1297924"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4980","DOI":"10.1021\/acs.jcim.4c00310","article-title":"MDF-DTA: A Multi-Dimensional Fusion Approach for Drug-Target Binding Affinity Prediction","volume":"64","author":"Ranjan","year":"2024","journal-title":"J. Chem. Inf. Model."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1097\/PAS.0000000000000086","article-title":"A Quantitative Histomorphometric Classifier (QuHbIC) Identifies Aggressive Versus Indolent P16-Positive Oropharyngeal Squamous Cell Carcinoma","volume":"38","author":"Lewis","year":"2014","journal-title":"Am. J. Surg. Pathol."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Choi, N., Kim, J., Yi, H., Kim, H., Kim, T.H., Chung, M.J., Ji, M., Kim, Z., and Son, Y.-I. (2023). The Use of Artificial Intelligence Models to Predict Survival in Patients with Laryngeal Squamous Cell Carcinoma. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-35627-1"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Zhang, Y.-F., Shen, Y.-J., Huang, Q., Wu, C.-P., Zhou, L., and Ren, H.-L. (2023). Predicting Survival of Advanced Laryngeal Squamous Cell Carcinoma: Comparison of Machine Learning Models and Cox Regression Models. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-45831-8"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"100173","DOI":"10.1016\/j.labinv.2023.100173","article-title":"Development of a Pathomics-Based Model for the Prediction of Malignant Transformation in Oral Leukoplakia","volume":"103","author":"Cai","year":"2023","journal-title":"Lab. Investig."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1038\/s41571-021-00560-7","article-title":"Predicting Cancer Outcomes with Radiomics and Artificial Intelligence in Radiology","volume":"19","author":"Bera","year":"2022","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"102202","DOI":"10.1016\/j.eclinm.2023.102202","article-title":"Artificial Intelligence Aided Precise Detection of Local Recurrence on MRI for Nasopharyngeal Carcinoma: A Multicenter Cohort Study","volume":"63","author":"OuYang","year":"2023","journal-title":"EClinicalMedicine"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Fatapour, Y., Abiri, A., Kuan, E.C., and Brody, J.P. (2023). Development of a Machine Learning Model to Predict Recurrence of Oral Tongue Squamous Cell Carcinoma. Cancers, 15.","DOI":"10.3390\/cancers15102769"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"L\u00f3pez-Cort\u00e9s, X.A., Matamala, F., Venegas, B., and Rivera, C. (2022). Machine-Learning Applications in Oral Cancer: A Systematic Review. Appl. Sci., 12.","DOI":"10.3390\/app12115715"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Villanueva-Bueno, C., Collado-Borrell, R., Escudero-Vilaplana, V., Revuelta-Herrero, J.L., Marzal-Alfaro, M.B., Gonz\u00e1lez-Haba, E., Arranz-Arija, J.\u00c1., Osorio, S., Herranz-Alonso, A., and Sanjurjo-Saez, M. (2022). A Smartphone App to Improve the Safety of Patients Undergoing Treatment with Oral Antineoplastic Agents: 4 Years of Experience in a University Hospital. Front. Public Health, 10.","DOI":"10.3389\/fpubh.2022.978783"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1188\/18.ONF.69-76","article-title":"Use of a Smartphone Application for Prompting Oral Medication Adherence Among Adolescents and Young Adults With Cancer","volume":"45","author":"Wu","year":"2018","journal-title":"Oncol. Nurs. Forum"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1111\/cts.12884","article-title":"Precision Medicine, AI, and the Future of Personalized Health Care","volume":"14","author":"Johnson","year":"2021","journal-title":"Clin. Transl. Sci."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Giansanti, D. (2023). Precision Medicine 2.0: How Digital Health and AI Are Changing the Game. J. Pers. Med., 13.","DOI":"10.3390\/jpm13071057"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Bibault, J.-E., Giraud, P., Housset, M., Durdux, C., Taieb, J., Berger, A., Coriat, R., Chaussade, S., Dousset, B., and Nordlinger, B. (2018). Deep Learning and Radiomics Predict Complete Response after Neo-Adjuvant Chemoradiation for Locally Advanced Rectal Cancer. Sci. Rep., 8.","DOI":"10.1038\/s41598-018-30657-6"},{"key":"ref_66","unstructured":"Edemekong, P.F., Annamaraju, P., Afzal, M., and Haydel, M.J. (2025). Health Insurance Portability and Accountability Act (HIPAA) Compliance. StatPearls, StatPearls Publishing."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1038\/s41746-021-00509-1","article-title":"Patient Apprehensions about the Use of Artificial Intelligence in Healthcare","volume":"4","author":"Richardson","year":"2021","journal-title":"Npj Digit. Med."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"e39748","DOI":"10.2196\/39748","article-title":"The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review","volume":"24","author":"Istasy","year":"2022","journal-title":"J. Med. Internet Res."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"010318","DOI":"10.7189\/jogh.09.020318","article-title":"Artificial Intelligence and Algorithmic Bias: Implications for Health Systems","volume":"9","author":"Panch","year":"2019","journal-title":"J. Glob. Health"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Kelly, C.J., Karthikesalingam, A., Suleyman, M., Corrado, G., and King, D. (2019). Key Challenges for Delivering Clinical Impact with Artificial Intelligence. BMC Med., 17.","DOI":"10.1186\/s12916-019-1426-2"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"106459","DOI":"10.1016\/j.oraloncology.2023.106459","article-title":"Machine Learning Driven Index of Tumor Multinucleation Correlates with Survival and Suppressed Anti-Tumor Immunity in Head and Neck Squamous Cell Carcinoma Patients","volume":"143","author":"Koyuncu","year":"2023","journal-title":"Oral Oncol."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Khoury, Z.H., Ferguson, A., Price, J.B., Sultan, A.S., and Wang, R. (2024). Responsible Artificial Intelligence for Addressing Equity in Oral Healthcare. Front. Oral Health, 5.","DOI":"10.3389\/froh.2024.1408867"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/s40537-023-00703-w","article-title":"Data-Centric Artificial Intelligence in Oncology: A Systematic Review Assessing Data Quality in Machine Learning Models for Head and Neck Cancer","volume":"10","author":"Adeoye","year":"2023","journal-title":"J. Big Data"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"e47031","DOI":"10.2196\/47031","article-title":"Trust in and Acceptance of Artificial Intelligence Applications in Medicine: Mixed Methods Study","volume":"11","author":"Shevtsova","year":"2024","journal-title":"JMIR Hum. Factors"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1001\/jama.2023.25054","article-title":"Will Generative Artificial Intelligence Deliver on Its Promise in Health Care?","volume":"331","author":"Wachter","year":"2024","journal-title":"JAMA"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1038\/s41698-025-00986-7","article-title":"Progress and Challenges of Artificial Intelligence in Lung Cancer Clinical Translation","volume":"9","author":"Zhu","year":"2025","journal-title":"NPJ Precis. Oncol."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1476","DOI":"10.1016\/S1470-2045(24)00497-2","article-title":"Validity and Timeliness of Cancer Diagnosis Data Collected during a Prospective Cohort Study and Reported by the English and Welsh Cancer Registries: A Retrospective, Comparative Analysis","volume":"25","author":"Jackson","year":"2024","journal-title":"Lancet Oncol."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Broggi, G., Maniaci, A., Lentini, M., Palicelli, A., Zanelli, M., Zizzo, M., Koufopoulos, N., Salzano, S., Mazzucchelli, M., and Caltabiano, R. (2024). Artificial Intelligence in Head and Neck Cancer Diagnosis: A Comprehensive Review with Emphasis on Radiomics, Histopathological, and Molecular Applications. Cancers, 16.","DOI":"10.3390\/cancers16213623"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"e221078","DOI":"10.1001\/jamanetworkopen.2022.1078","article-title":"Analysis of a Remote Monitoring Program for Symptoms Among Adults With Cancer Receiving Antineoplastic Therapy","volume":"5","author":"Daly","year":"2022","journal-title":"JAMA Netw. Open"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1016\/j.pmn.2023.04.001","article-title":"Implementing PainChek and PARO to Support Pain Assessment and Management in Residents with Dementia: A Qualitative Study","volume":"24","author":"Pu","year":"2023","journal-title":"Pain Manag. Nurs."},{"key":"ref_81","unstructured":"Tempus|AI-Enabled Precision Medicine (2025, April 07). Tempus. Available online: https:\/\/www.tempus.com\/."}],"container-title":["Cancers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6694\/17\/18\/3023\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:46:23Z","timestamp":1760035583000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6694\/17\/18\/3023"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,16]]},"references-count":81,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["cancers17183023"],"URL":"https:\/\/doi.org\/10.3390\/cancers17183023","relation":{},"ISSN":["2072-6694"],"issn-type":[{"value":"2072-6694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,16]]}}}