{"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":1790996417027,"version":"4.1.0"},"reference-count":97,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation of Korea (NRF)","award":["NRF-2021R1A6A1A03039493"],"award-info":[{"award-number":["NRF-2021R1A6A1A03039493"]}]},{"name":"National Research Foundation of Korea (NRF)","award":["NRF-2022R1A2C1004401"],"award-info":[{"award-number":["NRF-2022R1A2C1004401"]}]},{"DOI":"10.13039\/501100003725","name":"Ministry of Education","doi-asserted-by":"publisher","award":["NRF-2021R1A6A1A03039493"],"award-info":[{"award-number":["NRF-2021R1A6A1A03039493"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"Ministry of Education","doi-asserted-by":"publisher","award":["NRF-2022R1A2C1004401"],"award-info":[{"award-number":["NRF-2022R1A2C1004401"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"Korean government (MSIT)","doi-asserted-by":"publisher","award":["NRF-2021R1A6A1A03039493"],"award-info":[{"award-number":["NRF-2021R1A6A1A03039493"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"Korean government (MSIT)","doi-asserted-by":"publisher","award":["NRF-2022R1A2C1004401"],"award-info":[{"award-number":["NRF-2022R1A2C1004401"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Parkinson\u2019s disease and Alzheimer\u2019s disease are among the most common neurodegenerative disorders. These diseases are correlated with advancing age and are hence increasingly becoming prevalent in developed countries due to an increasingly aging demographic. Several tools are used to predict and diagnose these diseases, including pathological and genetic tests, radiological scans, and clinical examinations. Artificial intelligence is evolving to artificial general intelligence, which mimics the human learning process. Large language models can use an enormous volume of online and offline resources to gain knowledge and use it to perform different types of tasks. This work presents an understanding of two major neurodegenerative disorders, artificial general intelligence, and the efficacy of using artificial general intelligence in detecting and predicting these neurodegenerative disorders. A detailed discussion on detecting these neurodegenerative diseases using artificial general intelligence by analyzing diagnostic data is presented. An Internet of Things-based ubiquitous monitoring and treatment framework is presented. An outline for future research opportunities based on the challenges in this area is also presented.<\/jats:p>","DOI":"10.3390\/s24206658","type":"journal-article","created":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T10:11:04Z","timestamp":1729073464000},"page":"6658","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Artificial General Intelligence for the Detection of Neurodegenerative Disorders"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5708-1532","authenticated-orcid":false,"given":"Yazdan Ahmad","family":"Qadri","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Yeungnam University, Gyeongsan-si 38541, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1095-8445","authenticated-orcid":false,"given":"Khurshid","family":"Ahmad","sequence":"additional","affiliation":[{"name":"Department of Health Informatics, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8454-6980","authenticated-orcid":false,"given":"Sung Won","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Yeungnam University, Gyeongsan-si 38541, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1553\/p-8jf5-7cdc","article-title":"Immigration and the prospects for long-run population decreases in European countries","volume":"21","author":"Parr","year":"2023","journal-title":"Vienna Yearb. Popul. Res."},{"key":"ref_2","unstructured":"Jung, K.S. (2024, July 20). 2024 National Health Insurance & Long-Term Care Insurance System Republic of Korea. Available online: https:\/\/www.nhis.or.kr\/english\/wbheaa03500m01.do?mode=download&articleNo=10840421&attachNo=350606."},{"key":"ref_3","first-page":"123","article-title":"The Rising Prevalence of Parkinson\u2019s Disease in South Korea","volume":"17","author":"Kim","year":"2021","journal-title":"J. Clin. Neurol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kang, S.H., Moon, S.J., Kang, M., Chung, S.J., Cho, G.J., and Koh, S.B. (2023). Incidence of Parkinson\u2019s disease and modifiable risk factors in Korean population: A longitudinal follow-up study of a nationwide cohort. Front. Aging Neurosci., 15.","DOI":"10.3389\/fnagi.2023.1094778"},{"key":"ref_5","first-page":"658","article-title":"Disease burden of Parkinson\u2019s disease in China and its provinces from 1990 to 2021: Findings from the global burden of disease study 2021","volume":"19","author":"Xu","year":"2024","journal-title":"Lancet Reg. Health-West. Pac."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"658","DOI":"10.1002\/alz.12694","article-title":"Global estimates on the number of persons across the Alzheimer\u2019s disease continuum","volume":"19","author":"Gustavsson","year":"2023","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1093\/oxfordjournals.aje.a117721","article-title":"The Frequency of Idiopathic Parkinson\u2019s Disease by Age, Ethnic Group, and Sex in Northern Manhattan, 1988\u20131993","volume":"142","author":"Mayeux","year":"1995","journal-title":"Am. J. Epidemiol."},{"key":"ref_8","first-page":"189","article-title":"Alzheimer\u2019s Disease in South Korea: Current Status and Future Directions","volume":"49","author":"Lee","year":"2020","journal-title":"Dement. Geriatr. Cogn. Disord."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2865","DOI":"10.1002\/alz.12901","article-title":"The worldwide costs of dementia in 2019","volume":"19","author":"Wimo","year":"2023","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1038\/s41531-021-00266-8","article-title":"Mining imaging and clinical data with machine learning approaches for the diagnosis and early detection of Parkinson\u2019s disease","volume":"8","author":"Zhang","year":"2022","journal-title":"NPJ Parkinson\u2019s Dis."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"23735","DOI":"10.1007\/s11042-022-11925-0","article-title":"Early detection of Alzheimer\u2019s disease based on the state-of-the-art deep learning approach: A comprehensive survey","volume":"81","author":"Arafa","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/S1474-4422(21)00030-2","article-title":"Challenges in the diagnosis of Parkinson\u2019s disease","volume":"20","author":"Tolosa","year":"2021","journal-title":"Lancet Neurol."},{"key":"ref_13","first-page":"535","article-title":"NIA-AA Research Framework: Toward a Biological Definition of Alzheimer\u2019s Disease","volume":"16","author":"Jack","year":"2020","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Joshi, G., Jain, A., Araveeti, S.R., Adhikari, S., Garg, H., and Bhandari, M. (2024). FDA-Approved Artificial Intelligence and Machine Learning (AI\/ML)-Enabled Medical Devices: An Updated Landscape. Electronics, 13.","DOI":"10.3390\/electronics13030498"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e13235","DOI":"10.1111\/exsy.13235","article-title":"Optimizing CNN-LSTM hybrid classifier using HCA for biomedical image classification","volume":"40","author":"Pradhan","year":"2023","journal-title":"Expert Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Singh, A.K., and Krishnan, S. (2023). ECG signal feature extraction trends in methods and applications. BioMed. Eng. Online, 22.","DOI":"10.1186\/s12938-023-01075-1"},{"key":"ref_17","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_18","first-page":"50117","article-title":"Machine Learning Models for Parkinson Disease: Systematic Review","volume":"12","author":"Tabashum","year":"2024","journal-title":"Med. Inform."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"eado7069","DOI":"10.1126\/science.ado7069","article-title":"Debates on the nature of artificial general intelligence","volume":"383","author":"Mitchell","year":"2024","journal-title":"Science"},{"key":"ref_20","unstructured":"Ma, Y., Zhang, C., and Zhu, S.C. (2023). Brain in a Vat: On Missing Pieces Towards Artificial General Intelligence in Large Language Models. arXiv."},{"key":"ref_21","unstructured":"Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y.T., Li, Y., and Lundberg, S. (2023). Sparks of Artificial General Intelligence: Early experiments with GPT-4. arXiv."},{"key":"ref_22","unstructured":"Li, X., Zhang, L., Wu, Z., Liu, Z., Zhao, L., Yuan, Y., Liu, J., Li, G., Zhu, D., and Yan, P. (2023). Artificial General Intelligence for Medical Imaging. arXiv."},{"key":"ref_23","unstructured":"Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., Yogatama, D., Bosma, M., Zhou, D., and Metzler, D. (2022). Emergent Abilities of Large Language Models. arXiv."},{"key":"ref_24","unstructured":"Li, D., Kadav, A., Gao, A., Li, R., and Bourgon, R. (2024). Automated Clinical Data Extraction with Knowledge Conditioned LLMs. arXiv."},{"key":"ref_25","unstructured":"American College of Radiology, D.S.I. (2024, July 20). About ACR\u2019s Data Science Institute AI Central. Available online: https:\/\/aicentral.acrdsi.org\/About-us."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Erda\u015f, \u00c7.B., S\u00fcmer, E., and Kibaro\u011flu, S. (2021). Neurodegenerative disease detection and severity prediction using deep learning approaches. Biomed. Signal Process. Control, 70.","DOI":"10.1016\/j.bspc.2021.103069"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"81","DOI":"10.12703\/r\/10-81","article-title":"Recent advances in preventing neurodegenerative diseases","volume":"10","author":"Chou","year":"2021","journal-title":"Fac. Rev."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, X., Feng, X., Sun, X., Hou, N., Han, F., and Liu, Y. (2022). Global, regional, and national burden of Alzheimer\u2019s disease and other dementias, 1990\u20132019. Front. Aging Neurosci., 14.","DOI":"10.3389\/fnagi.2022.937486"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1016\/S0140-6736(14)61393-3","article-title":"Parkinson\u2019s disease","volume":"386","author":"Kalia","year":"2015","journal-title":"Lancet"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1016\/j.molmed.2006.09.007","article-title":"Understanding the molecular causes of Parkinson\u2019s disease","volume":"12","author":"Gandhi","year":"2006","journal-title":"Trends Mol. Med."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/S0140-6736(06)69113-7","article-title":"Alzheimer\u2019s disease","volume":"368","author":"Blennow","year":"2006","journal-title":"Lancet"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/S0140-6736(15)01124-1","article-title":"Alzheimer\u2019s disease","volume":"388","author":"Scheltens","year":"2016","journal-title":"Lancet"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"102357","DOI":"10.1016\/j.arr.2024.102357","article-title":"Neurodegenerative disorders: Mechanisms of degeneration and therapeutic approaches with their clinical relevance","volume":"99","author":"Gadhave","year":"2024","journal-title":"Ageing Res. Rev."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2219","DOI":"10.1038\/s41467-024-46554-8","article-title":"Neuropathogenesis-on-chips for neurodegenerative diseases","volume":"15","author":"Amartumur","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1016\/j.bbi.2023.11.005","article-title":"Machine learning and artificial intelligence in neuroscience: A primer for researchers","volume":"115","author":"Badrulhisham","year":"2024","journal-title":"Brain Behav. Immun."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"73","DOI":"10.4137\/BMI.S596","article-title":"Genomic and Proteomic Biomarker Discovery in Neurological Disease","volume":"3","author":"Robeson","year":"2008","journal-title":"Biomark. Insights"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"S42","DOI":"10.1038\/nm1064","article-title":"Stem cell therapy for human neurodegenerative disorders\u2013how to make it work","volume":"10","author":"Lindvall","year":"2004","journal-title":"Nat. Med."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1038\/s41582-023-00828-9","article-title":"Data-driven care for patients with neurodegenerative disorders","volume":"19","author":"Waldemar","year":"2023","journal-title":"Nat. Rev. Neurol."},{"key":"ref_39","unstructured":"Russell, S., and Norvig, P. (2021). Artificial Intelligence: A Modern Approach, Pearson. [4th ed.]."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Serey, J., Alfaro, M., Fuertes, G., Vargas, M., Dur\u00e1n, C., Ternero, R., Rivera, R., and Sabattin, J. (2023). Pattern Recognition and Deep Learning Technologies, Enablers of Industry 4.0, and Their Role in Engineering Research. Symmetry, 15.","DOI":"10.3390\/sym15020535"},{"key":"ref_41","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1016\/j.beth.2020.05.002","article-title":"Supervised Machine Learning: A Brief Primer","volume":"51","author":"Jiang","year":"2020","journal-title":"Behav. Ther."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.dsm.2021.12.002","article-title":"Machine learning-based approach: Global trends, research directions, and regulatory standpoints","volume":"4","author":"Pugliese","year":"2021","journal-title":"Data Sci. Manag."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Caruana, R., and Niculescu-Mizil, A. (2006, January 25\u201329). An empirical comparison of supervised learning algorithms. Proceedings of the 23rd International Conference on Machine Learning, ICML \u201906, New York, NY, USA.","DOI":"10.1145\/1143844.1143865"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Shehab, M., Abualigah, L., Shambour, Q., Abu-Hashem, M.A., Shambour, M.K.Y., Alsalibi, A.I., and Gandomi, A.H. (2022). Machine learning in medical applications: A review of state-of-the-art methods. Comput. Biol. Med., 145.","DOI":"10.1016\/j.compbiomed.2022.105458"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5541","DOI":"10.1002\/sim.9924","article-title":"Unsupervised learning for medical data: A review of probabilistic factorization methods","volume":"42","author":"Neijzen","year":"2023","journal-title":"Stat. Med."},{"key":"ref_47","unstructured":"Pandey, R., Khatri, S.K., Singh, N.K., and Verma, P. (2022). Unsupervised Learning. Artificial Intelligence and Machine Learning for EDGE Computing, Academic Press."},{"key":"ref_48","unstructured":"Singh, D.B., and Pathak, R.K. (2022). Machine learning in bioinformatics. Bioinformatics, Academic Press."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Gui, J., Chen, T., Zhang, J., Cao, Q., Sun, Z., Luo, H., and Tao, D. (2024). A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends. IEEE Trans. Pattern Anal. Mach. Intell., 1\u201320.","DOI":"10.1109\/TPAMI.2024.3415112"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.3233\/JIFS-169689","article-title":"Semi-supervised regression: A recent review","volume":"35","author":"Kostopoulos","year":"2018","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_51","unstructured":"Grandvalet, Y., and Bengio, Y. (2004, January 13\u201318). Semi-supervised learning by entropy minimization. Proceedings of the 17th International Conference on Neural Information Processing Systems, NIPS\u201904, Cambridge, MA, USA."},{"key":"ref_52","unstructured":"Lee, D.H. (2013, January 16\u201321). Pseudo-Label: The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks. Proceedings of the ICML 2013 Workshop: Challenges in Representation Learning (WREPL), Atlanta, GA, USA."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"82146","DOI":"10.1109\/ACCESS.2021.3084358","article-title":"A Survey on Semi-, Self- and Unsupervised Learning for Image Classification","volume":"9","author":"Schmarje","year":"2021","journal-title":"IEEE Access"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1613\/jair.301","article-title":"Reinforcement learning: A survey","volume":"4","author":"Kaelbling","year":"1996","journal-title":"J. Artif. Intell. Res."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"209320","DOI":"10.1109\/ACCESS.2020.3038605","article-title":"A Gentle Introduction to Reinforcement Learning and its Application in Different Fields","volume":"8","author":"Naeem","year":"2020","journal-title":"IEEE Access"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Gu, S., Yang, L., Du, Y., Chen, G., Walter, F., Wang, J., and Knoll, A. (2024). A Review of Safe Reinforcement Learning: Methods, Theory and Applications. arXiv.","DOI":"10.1109\/TPAMI.2024.3457538"},{"key":"ref_57","unstructured":"Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., and Zhou, E. (2023). The Rise and Potential of Large Language Model Based Agents: A Survey. arXiv."},{"key":"ref_58","unstructured":"Biever, C. (2024, July 30). CHATGPT Broke the Turing Test\u2014The Race Is on for New Ways to Assess AI. Available online: https:\/\/www.nature.com\/articles\/d41586-023-02361-7."},{"key":"ref_59","unstructured":"NVIDIA (2024, July 30). Large Language Models. Available online: https:\/\/www.nvidia.com\/en-us\/glossary\/large-language-models\/."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1016\/j.procir.2021.03.088","article-title":"A survey on long short-term memory networks for time series prediction","volume":"99","author":"Lindemann","year":"2021","journal-title":"Procedia CIRP"},{"key":"ref_61","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2023). Attention Is All You Need. arXiv."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1058","DOI":"10.1212\/WNL.0000000000207967","article-title":"Large Language Models in Neurology Research and Future Practice","volume":"101","author":"Romano","year":"2023","journal-title":"Neurology"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1007\/s13139-024-00857-2","article-title":"Contribution of ChatGPT in Parkinson\u2019s Disease Detection","volume":"58","author":"Aggarwal","year":"2024","journal-title":"Nucl. Med. Mol. Imaging"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Stroganov, O., Schedlbauer, A., Lorenzen, E., Kadhim, A., Lobanova, A., Lewis, D.A., and Glausier, J.R. (2023). Unpacking Unstructured Data: A Pilot Study on Extracting Insights from Neuropathological Reports of Parkinson\u2019s Disease Patients using Large Language Models. bioRxiv.","DOI":"10.1101\/2023.09.12.557252"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/B978-0-444-63639-3.00004-9","article-title":"Chapter 4\u2014The NIH NeuroBioBank: Creating opportunities for human brain research","volume":"Volume 150","author":"Huitinga","year":"2018","journal-title":"Brain Banking"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"e200334","DOI":"10.1212\/CPJ.0000000000200334","article-title":"Cognitive Symptoms in Cross-Sectional Parkinson Disease Cohort Evaluated by Human-in-the-Loop Machine Learning and Natural Language Processing","volume":"14","author":"Purks","year":"2024","journal-title":"Neurol. Clin. Pract."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2014","DOI":"10.1093\/jamia\/ocac168","article-title":"Multimodal attention-based deep learning for Alzheimer\u2019s disease diagnosis","volume":"29","author":"Golovanevsky","year":"2022","journal-title":"J. Am. Med Inform. Assoc."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"3404","DOI":"10.1038\/s41467-022-31037-5","article-title":"Multimodal deep learning for Alzheimer\u2019s disease dementia assessment","volume":"13","author":"Qiu","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.jalz.2005.06.003","article-title":"Ways toward an early diagnosis in Alzheimer\u2019s disease: The Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI)","volume":"1","author":"Mueller","year":"2005","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Cardenas, L., Parajes, K., Zhu, M., and Zhai, S. (2024, January 8\u201310). AutoHealth: Advanced LLM-Empowered Wearable Personalized Medical Butler for Parkinson\u2019s Disease Management. Proceedings of the 2024 IEEE 14th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA.","DOI":"10.1109\/CCWC60891.2024.10427622"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Liu, N., Luo, K., Yuan, Z., and Chen, Y. (2022). A Transfer Learning Method for Detecting Alzheimer\u2019s Disease Based on Speech and Natural Language Processing. Front. Public Health, 10.","DOI":"10.3389\/fpubh.2022.772592"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Roshanzamir, A., Aghajan, H., and Baghshah, S. (2021). Transformer-based deep neural network language models for Alzheimer\u2019s disease risk assessment from targeted speech. BMC Med. Inform. Decis. Mak., 21.","DOI":"10.1186\/s12911-021-01456-3"},{"key":"ref_73","unstructured":"DementiaBank (2024, July 30). DementiaBank English Pitt Corpus. Available online: https:\/\/dementia.talkbank.org\/access\/English\/Pitt.html."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1001\/archneur.1994.00540180063015","article-title":"The Natural History of Alzheimer\u2019s Disease: Description of Study Cohort and Accuracy of Diagnosis","volume":"51","author":"Becker","year":"1994","journal-title":"Arch. Neurol."},{"key":"ref_75","unstructured":"Jurafsky, D., Chai, J., Schluter, N., and Tetreault, J. (2020, January 5\u201310). A Tale of Two Perplexities: Sensitivity of Neural Language Models to Lexical Retrieval Deficits in Dementia of the Alzheimer\u2019s Type. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Balagopalan, A., and Novikova, J. (2022). Comparing Acoustic-based Approaches for Alzheimer\u2019s Disease Detection. arXiv.","DOI":"10.21437\/Interspeech.2021-759"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"5262","DOI":"10.1002\/alz.13886","article-title":"Prediction of Alzheimer\u2019s disease progression within 6 years using speech: A novel approach leveraging language models","volume":"20","author":"Amini","year":"2024","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Martinc, M., and Pollak, S. (2020, January 25\u201329). Tackling the ADReSS Challenge: A Multimodal Approach to the Automated Recognition of Alzheimer\u2019s Dementia. Proceedings of the Interspeech 2020, Shanghai, China.","DOI":"10.21437\/Interspeech.2020-2202"},{"key":"ref_79","unstructured":"DementiaBank (2024, July 30). ADReSS 2020 Challenge. Available online: https:\/\/dementia.talkbank.org\/ADReSS-2020\/."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Yuan, J., Bian, Y., Cai, X., Huang, J., Ye, Z., and Church, K. (2020, January 25\u201329). Disfluencies and Fine-Tuning Pre-Trained Language Models for Detection of Alzheimer\u2019s Disease. Proceedings of the Interspeech 2020, Shanghai, China.","DOI":"10.21437\/Interspeech.2020-2516"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Balagopalan, A., Eyre, B., Rudzicz, F., and Novikova, J. (2020, January 25\u201329). To BERT or not to BERT: Comparing Speech and Language-Based Approaches for Alzheimer\u2019s Disease Detection. Proceedings of the Interspeech 2020, Shanghai, China.","DOI":"10.21437\/Interspeech.2020-2557"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Pappagari, R., Cho, J., Moro-Vel\u00e1zquez, L., and Dehak, N. (2020, January 25\u201329). Using State of the Art Speaker Recognition and Natural Language Processing Technologies to Detect Alzheimer\u2019s Disease and Assess its Severity. Proceedings of the Interspeech 2020, Shanghai, China.","DOI":"10.21437\/Interspeech.2020-2587"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Koo, J., Lee, J.H., Pyo, J., Jo, Y., and Lee, K. (2020, January 25\u201329). Exploiting Multi-Modal Features from Pre-Trained Networks for Alzheimer\u2019s Dementia Recognition. Proceedings of the Interspeech 2020, Shanghai, China.","DOI":"10.21437\/Interspeech.2020-3153"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Searle, T., Ibrahim, Z., and Dobson, R. (2020, January 25\u201329). Comparing Natural Language Processing Techniques for Alzheimer\u2019s Dementia Prediction in Spontaneous Speech. Proceedings of the Interspeech 2020, Shanghai, China.","DOI":"10.21437\/Interspeech.2020-2729"},{"key":"ref_85","unstructured":"Rao, H., Zeng, M., Zhao, X., and Miao, C. (2024). A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis. arXiv."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1038\/s41746-023-00868-x","article-title":"Challenges of implementing computer-aided diagnostic models for neuroimages in a clinical setting","volume":"6","author":"Leming","year":"2023","journal-title":"Npj Digit. Med."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1038\/s41582-020-0377-8","article-title":"Applications of machine learning to diagnosis and treatment of Neurodegenerative Diseases","volume":"16","author":"Myszczynska","year":"2020","journal-title":"Nat. Rev. Neurol."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1109\/MNET.2024.3352598","article-title":"Toward the Internet of Medical Things for Real-Time Health Monitoring over Wi-Fi","volume":"38","author":"Qadri","year":"2024","journal-title":"IEEE Netw."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.1109\/COMST.2020.2973314","article-title":"The Future of Healthcare Internet of Things: A Survey of Emerging Technologies","volume":"22","author":"Qadri","year":"2020","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_90","unstructured":"Kim, S.W., Qadri, Y.A., Bajracharaya, R., and Nauman, A. (2021). Method and Apparatus for Deep Brain Stimulation Using Deep Learning and Recording Medium for Performing the Method. Korean Intellectual Property. (10-2247761), Office Patent."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1038\/nrn2196","article-title":"Translational principles of deep brain stimulation","volume":"8","author":"Kringelbach","year":"2007","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Qadri, Y.A., Nauman, A., Musaddiq, A., Garcia-Villegas, E., and Kim, S.W. (2022). Preparing Wi-Fi 7 for Healthcare Internet-of-Things. Sensors, 22.","DOI":"10.3390\/s22166209"},{"key":"ref_93","unstructured":"NVIDIA (2024, July 30). NVIDIA Blackwell Platform Arrives to Power a New Era of Computing. Available online: https:\/\/nvidianews.nvidia.com\/news\/nvidia-blackwell-platform-arrives-to-power-a-new-era-of-computing."},{"key":"ref_94","unstructured":"Wu, K., Wu, E., and Zou, J. (2024). ClashEval: Quantifying the tug-of-war between an LLM\u2019s internal prior and external evidence. arXiv."},{"key":"ref_95","unstructured":"Chaudhari, S., Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., Deshpande, A., and da Silva, B.C. (2024). RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs. arXiv."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1126\/science.aax2342","article-title":"Dissecting racial bias in an algorithm used to manage the health of populations","volume":"366","author":"Obermeyer","year":"2019","journal-title":"Science"},{"key":"ref_97","first-page":"1329","article-title":"Re-Identification Risk in HIPAA De-Identified Datasets: The MVA Attack","volume":"2018","author":"Victor","year":"2018","journal-title":"AMIA Annu. Symp. Proc."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/20\/6658\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:14:11Z","timestamp":1760112851000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/20\/6658"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"references-count":97,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["s24206658"],"URL":"https:\/\/doi.org\/10.3390\/s24206658","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}