{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T03:00:17Z","timestamp":1790996417048,"version":"4.1.0"},"reference-count":58,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T00:00:00Z","timestamp":1758758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Brain Sciences"],"abstract":"<jats:p>The rapid advancements in computer processing, algorithmic development, and the availability of large-scale datasets have positioned Artificial Intelligence (AI) as a valuable tool across multiple domains, including Medicine. In the field of Nuclear Medicine neuroimaging, with Positron Emission Tomography (PET), AI has demonstrated significant potential in improving diagnostic accuracy for neurodegenerative cognitive disorders. This is especially relevant for the early diagnosis, preclinical detection, and prediction of disease progression in Alzheimer\u2019s disease (AD), the most prevalent form of cognitive impairment in individuals over 65 years of age. This narrative review aims to synthesize current advances, explore future directions, and highlight outstanding challenges in the application of Artificial Intelligence to PET imaging for the clinical management of Alzheimer\u2019s disease, with particular focus on three key modalities: 18F-FDG PET, Amyloid PET, and Tau PET.<\/jats:p>","DOI":"10.3390\/brainsci15101038","type":"journal-article","created":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T08:56:44Z","timestamp":1758790604000},"page":"1038","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Artificial Intelligence in PET Imaging for Alzheimer\u2019s Disease: A Narrative Review"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7038-3615","authenticated-orcid":false,"given":"Andrea","family":"Marongiu","sequence":"first","affiliation":[{"name":"Unit of Nuclear Medicine, Department of Medicine, Surgery and Pharmacy, University of Sassari, Viale San Pietro 8, 07100 Sassari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5753-399X","authenticated-orcid":false,"given":"Angela","family":"Spanu","sequence":"additional","affiliation":[{"name":"Unit of Nuclear Medicine, Department of Medicine, Surgery and Pharmacy, University of Sassari, Viale San Pietro 8, 07100 Sassari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Barbara","family":"Palumbo","sequence":"additional","affiliation":[{"name":"Section of Nuclear Medicine and Health Physics, Department of Medicine and Surgery, Universit\u00e0 degli Studi di Perugia, Piazza Lucio Severi 1, 06132 Perugia, Italy"},{"name":"Nuclear Medicine Unit, Perugia Hospital, Piazzale G. Menghini 3, 06132 Perugia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3371-1928","authenticated-orcid":false,"given":"Francesco","family":"Bianconi","sequence":"additional","affiliation":[{"name":"Department of Engineering, Universit\u00e0 degli Studi di Perugia, Via Goffredo Duranti 93, 06125 Perugia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4423-5496","authenticated-orcid":false,"given":"Luca","family":"Filippi","sequence":"additional","affiliation":[{"name":"Department of Biomedicine and Prevention, University of Rome \u201cTor Vergata\u201d, Via Montpellier 1, 00133 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giuseppe","family":"Madeddu","sequence":"additional","affiliation":[{"name":"Unit of Nuclear Medicine, Department of Medicine, Surgery and Pharmacy, University of Sassari, Viale San Pietro 8, 07100 Sassari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7194-985X","authenticated-orcid":false,"given":"Susanna","family":"Nuvoli","sequence":"additional","affiliation":[{"name":"Unit of Nuclear Medicine, Department of Medicine, Surgery and Pharmacy, University of Sassari, Viale San Pietro 8, 07100 Sassari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2673","DOI":"10.1016\/S0140-6736(17)31363-6","article-title":"Dementia Prevention, Intervention, and Care","volume":"390","author":"Livingston","year":"2017","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/S1474-4422(18)30403-4","article-title":"Global, Regional, and National Burden of Alzheimer\u2019s Disease and Other Dementias, 1990\u20132016: A Systematic Analysis for the Global Burden of Disease Study 2016","volume":"18","author":"Nichols","year":"2019","journal-title":"Lancet Neurol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5143","DOI":"10.1002\/alz.13859","article-title":"Revised Criteria for Diagnosis and Staging of Alzheimer\u2019s Disease: Alzheimer\u2019s Association Workgroup","volume":"20","author":"Jack","year":"2024","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.cger.2017.02.005","article-title":"Mild Cognitive Impairment","volume":"33","author":"Sanford","year":"2017","journal-title":"Clin. Geriatr. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/s10072-018-3620-y","article-title":"Subjective Cognitive Decline: Preclinical Manifestation of Alzheimer\u2019s Disease","volume":"40","author":"Lin","year":"2019","journal-title":"Neurol. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/j.jalz.2018.02.018","article-title":"NIA-AA Research Framework: Toward a Biological Definition of Alzheimer\u2019s Disease","volume":"14","author":"Jack","year":"2018","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Earnest, T., Yang, B., Kothapalli, D., and Sotiras, A. (2024). Alzheimer\u2019s Disease Neuroimaging Initiative Comprehensive Evaluation of AT(N) Imaging Biomarkers for Predicting Cognition. medRxiv.","DOI":"10.1101\/2024.11.25.24317943"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1879","DOI":"10.1002\/alz.12348","article-title":"Theoretical Impact of the AT(N) Framework on Dementia Using a Community Autopsy Sample","volume":"17","author":"Burke","year":"2021","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1007\/s00259-022-05947-x","article-title":"An EANM Position Paper on the Application of Artificial Intelligence in Nuclear Medicine","volume":"50","author":"Hustinx","year":"2022","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Silva-Sp\u00ednola, A., Baldeiras, I., Arrais, J.P., and Santana, I. (2022). The Road to Personalized Medicine in Alzheimer\u2019s Disease: The Use of Artificial Intelligence. Biomedicines, 10.","DOI":"10.3390\/biomedicines10020315"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Reel, P.S., Reel, S., Pearson, E., Trucco, E., and Jefferson, E. (2021). Using Machine Learning Approaches for Multi-Omics Data Analysis: A Review. Biotechnol. Adv., 49.","DOI":"10.1016\/j.biotechadv.2021.107739"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"77131","DOI":"10.1109\/ACCESS.2020.2989396","article-title":"Alzheimer\u2019s Diseases Detection by Using Deep Learning Algorithms: A Mini-Review","volume":"8","author":"Rassem","year":"2020","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Jo, T., Nho, K., and Saykin, A.J. (2019). Deep Learning in Alzheimer\u2019s Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data. Front. Aging Neurosci., 11.","DOI":"10.3389\/fnagi.2019.00220"},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"5117","DOI":"10.1007\/s10072-024-07649-8","article-title":"AI for the Prediction of Early Stages of Alzheimer\u2019s Disease from Neuroimaging Biomarkers\u2014A Narrative Review of a Growing Field","volume":"45","author":"Rudroff","year":"2024","journal-title":"Neurol. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"822","DOI":"10.21037\/atm-20-6220","article-title":"Artificial Intelligence for Molecular Neuroimaging","volume":"9","author":"Boyle","year":"2021","journal-title":"Ann. Transl. Med."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Mirkin, S., and Albensi, B.C. (2023). Should Artificial Intelligence Be Used in Conjunction with Neuroimaging in the Diagnosis of Alzheimer\u2019s Disease?. Front. Aging Neurosci., 15.","DOI":"10.3389\/fnagi.2023.1094233"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"102928","DOI":"10.1016\/j.artmed.2024.102928","article-title":"A Systematic Literature Review on the Significance of Deep Learning and Machine Learning in Predicting Alzheimer\u2019s Disease","volume":"154","author":"Kaur","year":"2024","journal-title":"Artif. Intell. Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1016\/j.neuroimage.2011.01.008","article-title":"Alzheimer\u2019s Disease Neuroimaging Initiative Multimodal Classification of Alzheimer\u2019s Disease and Mild Cognitive Impairment","volume":"55","author":"Zhang","year":"2011","journal-title":"Neuroimage"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.nicl.2014.08.023","article-title":"Random Forest Ensembles for Detection and Prediction of Alzheimer\u2019s Disease with a Good between-Cohort Robustness","volume":"6","author":"Lebedev","year":"2014","journal-title":"Neuroimage Clin."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.patcog.2007.05.018","article-title":"A Survey of Kernel and Spectral Methods for Clustering","volume":"41","author":"Filippone","year":"2008","journal-title":"Pattern Recognit."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"137530","DOI":"10.1016\/j.neulet.2023.137530","article-title":"Deep Learning Based Diagnosis of Alzheimer\u2019s Disease Using FDG-PET Images","volume":"817","author":"Kishore","year":"2023","journal-title":"Neurosci. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"102497","DOI":"10.1016\/j.arr.2024.102497","article-title":"AI-Driven Innovations in Alzheimer\u2019s Disease: Integrating Early Diagnosis, Personalized Treatment, and Prognostic Modelling","volume":"101","author":"Kale","year":"2024","journal-title":"Ageing Res. Rev."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Guo, Q., Zhang, Y., Zheng, J., Yang, Y., Du, X., Feng, H., and Zhang, S. (2023). Application of Deep Learning for Prediction of Alzheimer\u2019s Disease in PET\/MR Imaging. Bioengineering, 10.","DOI":"10.3390\/bioengineering10101120"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Nuvoli, S., Bianconi, F., Rondini, M., Lazzarato, A., Marongiu, A., Fravolini, M.L., Cascianelli, S., Amici, S., Filippi, L., and Spanu, A. (2022). Differential Diagnosis of Alzheimer Disease vs. Mild Cognitive Impairment Based on Left Temporal Lateral Lobe Hypomethabolism on 18F-FDG PET\/CT and Automated Classifiers. Diagnostics, 12.","DOI":"10.3390\/diagnostics12102425"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1148\/radiol.2018180958","article-title":"A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain","volume":"290","author":"Ding","year":"2019","journal-title":"Radiology"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1007\/s00259-023-06440-9","article-title":"Distinct Subtypes of Spatial Brain Metabolism Patterns in Alzheimer\u2019s Disease Identified by Deep Learning-Based FDG PET Clusters","volume":"51","author":"Ryoo","year":"2024","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1097\/RLU.0000000000002747","article-title":"The Use of Random Forests to Classify Amyloid Brain PET","volume":"44","author":"Zukotynski","year":"2019","journal-title":"Clin. Nucl. Med."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.1097\/MNM.0000000000001904","article-title":"Deep Learning-Based Binary Classification of Beta-Amyloid Plaques Using 18 F Florapronol PET","volume":"45","author":"An","year":"2024","journal-title":"Nucl. Med. Commun."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3950","DOI":"10.1093\/cercor\/bhab061","article-title":"Quantitative Radiomic Features as New Biomarkers for Alzheimer\u2019s Disease: An Amyloid PET Study","volume":"31","author":"Ding","year":"2021","journal-title":"Cereb. Cortex"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Jiao, F., Wang, M., Sun, X., Ju, Z., Lu, J., Wang, L., Jiang, J., and Zuo, C. (2023). Based on Tau PET Radiomics Analysis for the Classification of Alzheimer\u2019s Disease and Mild Cognitive Impairment. Brain Sci., 13.","DOI":"10.3390\/brainsci13020367"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1097\/WCO.0000000000001198","article-title":"Advanced Brain Imaging for the Diagnosis of Alzheimer Disease","volume":"36","author":"Wang","year":"2023","journal-title":"Curr. Opin. Neurol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Park, S.W., Yeo, N.Y., Lee, J., Lee, S.-H., Byun, J., Park, D.Y., Yum, S., Kim, J.-K., Byeon, G., and Kim, Y. (2023). Machine Learning Application for Classification of Alzheimer\u2019s Disease Stages Using 18F-Flortaucipir Positron Emission Tomography. Biomed. Eng. Online, 22.","DOI":"10.1186\/s12938-023-01107-w"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ruwanpathirana, G.P., Williams, R.C., Masters, C.L., Rowe, C.C., Johnston, L.A., and Davey, C.E. (2022). Mapping the Association between Tau-PET and A\u03b2-Amyloid-PET Using Deep Learning. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-18963-6"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"980","DOI":"10.1093\/brain\/awad346","article-title":"Synthesizing Images of Tau Pathology from Cross-Modal Neuroimaging Using Deep Learning","volume":"147","author":"Lee","year":"2024","journal-title":"Brain"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1038\/nn.4478","article-title":"Building Better Biomarkers: Brain Models in Translational Neuroimaging","volume":"20","author":"Woo","year":"2017","journal-title":"Nat. Neurosci."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Gupta, Y., Lama, R.K., and Kwon, G.-R. (2019). Alzheimer\u2019s Disease Neuroimaging Initiative Prediction and Classification of Alzheimer\u2019s Disease Based on Combined Features From Apolipoprotein-E Genotype, Cerebrospinal Fluid, MR, and FDG-PET Imaging Biomarkers. Front. Comput. Neurosci., 13.","DOI":"10.3389\/fncom.2019.00072"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5154","DOI":"10.1016\/j.acra.2024.06.040","article-title":"Combined Quantitative Amyloid-\u03b2 PET and Structural MRI Features Improve Alzheimer\u2019s Disease Classification in Random Forest Model\u2014A Multicenter Study","volume":"31","author":"Bao","year":"2024","journal-title":"Acad. Radiol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"198","DOI":"10.2174\/1567205013666160620122926","article-title":"Role of Artificial Intelligence Techniques (Automatic Classifiers) in Molecular Imaging Modalities in Neurodegenerative Diseases","volume":"14","author":"Cascianelli","year":"2017","journal-title":"Curr. Alzheimer Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2134","DOI":"10.4103\/1673-5374.367840","article-title":"Detection of Alzheimer\u2019s Disease Onset Using MRI and PET Neuroimaging: Longitudinal Data Analysis and Machine Learning","volume":"18","author":"Aberathne","year":"2023","journal-title":"Neural Regen. Res."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Aljuhani, M., Ashraf, A., and Edison, P. (2024). Use of Artificial Intelligence in Imaging Dementia. Cells, 13.","DOI":"10.3390\/cells13231965"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.trsl.2018.01.001","article-title":"Use of Multimodality Imaging and Artificial Intelligence for Diagnosis and Prognosis of Early Stages of Alzheimer\u2019s Disease","volume":"194","author":"Liu","year":"2018","journal-title":"Transl. Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.jalz.2015.06.1896","article-title":"The Global Alzheimer\u2019s Association Interactive Network","volume":"12","author":"Toga","year":"2016","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1212\/WNL.0b013e3181cb3e25","article-title":"Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI): Clinical Characterization","volume":"74","author":"Petersen","year":"2010","journal-title":"Neurology"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1017\/S1041610209009405","article-title":"The Australian Imaging, Biomarkers and Lifestyle (AIBL) Study of Aging: Methodology and Baseline Characteristics of 1112 Individuals Recruited for a Longitudinal Study of Alzheimer\u2019s Disease","volume":"21","author":"Ellis","year":"2009","journal-title":"Int. Psychogeriatr."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"e14378","DOI":"10.1002\/alz.14378","article-title":"Positron Emission Tomography Harmonization in the Alzheimer\u2019s Disease Neuroimaging Initiative: A Scalable and Rigorous Approach to Multisite Amyloid and Tau Quantification","volume":"21","author":"Landau","year":"2025","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"829","DOI":"10.2967\/jnumed.123.266463","article-title":"Evaluation of Neurodegenerative Disorders with Amyloid-\u03b2, Tau, and Dopaminergic PET Imaging: Interpretation Pitfalls","volume":"65","author":"Burkett","year":"2024","journal-title":"J. Nucl. Med."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1016\/j.neuroimage.2018.10.003","article-title":"Machine Learning in Neuroimaging: Progress and Challenges","volume":"197","author":"Davatzikos","year":"2019","journal-title":"Neuroimage"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Taiyeb Khosroshahi, M., Morsali, S., Gharakhanlou, S., Motamedi, A., Hassanbaghlou, S., Vahedi, H., Pedrammehr, S., Kabir, H.M.D., and Jafarizadeh, A. (2025). Explainable Artificial Intelligence in Neuroimaging of Alzheimer\u2019s Disease. Diagnostics, 15.","DOI":"10.3390\/diagnostics15050612"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.dsp.2017.10.011","article-title":"Methods for Interpreting and Understanding Deep Neural Networks","volume":"73","author":"Montavon","year":"2018","journal-title":"Digit. Signal Process."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Arras, L., Horn, F., Montavon, G., M\u00fcller, K.-R., and Samek, W. (2017). \u201cWhat Is Relevant in a Text Document?\u201d: An Interpretable Machine Learning Approach. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0181142"},{"key":"ref_52","unstructured":"Samek, W., Binder, A., Montavon, G., Bach, S., and M\u00fcller, K.-R. (2015). Evaluating the Visualization of What a Deep Neural Network Has Learned. arXiv."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"e240300","DOI":"10.1148\/ryai.240300","article-title":"CLAIM 2024 Update Panel Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update","volume":"6","author":"Tejani","year":"2024","journal-title":"Radiol. Artif. Intell."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1038\/s41591-024-03425-5","article-title":"The TRIPOD-LLM Reporting Guideline for Studies Using Large Language Models","volume":"31","author":"Gallifant","year":"2025","journal-title":"Nat. Med."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s13063-020-04951-6","article-title":"Reporting Guidelines for Clinical Trials of Artificial Intelligence Interventions: The SPIRIT-AI and CONSORT-AI Guidelines","volume":"22","author":"Ibrahim","year":"2021","journal-title":"Trials"},{"key":"ref_56","unstructured":"(2025, August 25). Ethics Guidelines for Trustworthy AI|Shaping Europe\u2019s Digital Future. Available online: https:\/\/digital-strategy.ec.europa.eu\/en\/library\/ethics-guidelines-trustworthy-ai."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"World Medical Association (2013). World Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. JAMA, 310, 2191\u20132194.","DOI":"10.1001\/jama.2013.281053"},{"key":"ref_58","unstructured":"(2025, August 25). CDC Health Insurance Portability and Accountability Act of 1996 (HIPAA), Available online: https:\/\/www.cdc.gov\/phlp\/php\/resources\/health-insurance-portability-and-accountability-act-of-1996-hipaa.html."}],"container-title":["Brain Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3425\/15\/10\/1038\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:49:22Z","timestamp":1760035762000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3425\/15\/10\/1038"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,25]]},"references-count":58,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["brainsci15101038"],"URL":"https:\/\/doi.org\/10.3390\/brainsci15101038","relation":{},"ISSN":["2076-3425"],"issn-type":[{"value":"2076-3425","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,25]]}}}