Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. Artificial Intelligence (AI) in healthcare is the use of computational algorithms that mimic human cognitive functions to analyze complex medical data. AI technologies like machine learning (ML) support the integration of biological, psychological, and social factors when approaching diagnosis, prognosis, and treatment of disease. This paper serves to acquaint clinicians and other stakeholders with the use, benefits, and limitations of AI for predicting, diagnosing, and classifying mild and major neurocognitive impairments, by providing a conceptual overview of this topic with emphasis on the features explored and AI techniques employed. We present studies that fell into six categories of features used for these purposes: (1) sociodemographics; (2) clinical and psychometric assessments; (3) neuroimaging and neurophysiology; (4) electronic health records and claims; (5) novel assessments (e.g., sensors for digital data); and (6) genomics/other omics. For each category we provide examples of AI approaches, including supervised and unsupervised ML, deep learning, and natural language processing. AI technology, still nascent in healthcare, has great potential to transform the way we diagnose and treat patients with neurocognitive disorders.
Keywords: Dementia; Machine learning; Mild cognitive impairment; Natural language processing; Sensors.
Copyright © 2019 Elsevier B.V. All rights reserved.
Conflict of interest statement
Declaration of Competing Interest Authors YY and HK are employees of IBM. The other authors have no conflicts of interest to report.
Artificial intelligence as an emerging technology in the current care of neurological disorders.J Neurol. 2019 Aug 26. doi: 10.1007/s00415-019-09518-3. Online ahead of print. J Neurol. 2019. PMID: 31451912 Review.
Artificial Intelligence for Mental Health and Mental Illnesses: an Overview.Curr Psychiatry Rep. 2019 Nov 7;21(11):116. doi: 10.1007/s11920-019-1094-0. Curr Psychiatry Rep. 2019. PMID: 31701320 Review.
Artificial intelligence in healthcare: past, present and future.Stroke Vasc Neurol. 2017 Jun 21;2(4):230-243. doi: 10.1136/svn-2017-000101. eCollection 2017 Dec. Stroke Vasc Neurol. 2017. PMID: 29507784 Free PMC article. Review.
Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Computing: What Do These Terms Mean and How Will They Impact Health Care?J Arthroplasty. 2018 Aug;33(8):2358-2361. doi: 10.1016/j.arth.2018.02.067. Epub 2018 Feb 27. J Arthroplasty. 2018. PMID: 29656964
Artificial intelligence: A joint narrative on potential use in pediatric stem and immune cell therapies and regenerative medicine.Transfus Apher Sci. 2018 Jun;57(3):422-424. doi: 10.1016/j.transci.2018.05.004. Epub 2018 May 9. Transfus Apher Sci. 2018. PMID: 29784537 Review.
Cited by 1 article
Digital Mental Health and COVID-19: Using Technology Today to Accelerate the Curve on Access and Quality Tomorrow.JMIR Ment Health. 2020 Mar 26;7(3):e18848. doi: 10.2196/18848. JMIR Ment Health. 2020. PMID: 32213476 Free PMC article.