Department of Psychiatry, University of California San Diego, La Jolla, CA, United States; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, United States; IBM-UCSD Artificial Intelligence for Healthy Living Program, La Jolla, CA, United States.
Department of Psychiatry, University of California San Diego, La Jolla, CA, United States; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, United States; IBM-UCSD Artificial Intelligence for Healthy Living Program, La Jolla, CA, United States; VA San Diego Healthcare System, San Diego, CA, United States.
Psychiatry Res. 2020 Feb;284:112732. doi: 10.1016/j.psychres.2019.112732. Epub 2019 Dec 9.
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.
保持认知和心智能力对于自主衰老至关重要。早期发现病理性认知能力下降有助于最大限度地发挥恢复性或预防性治疗的作用。人工智能（AI）在医疗保健中的应用是使用计算算法来模拟人类认知功能，以分析复杂的医疗数据。人工智能技术，如机器学习（ML），在考虑疾病的诊断、预后和治疗时，支持整合生物、心理和社会因素。本文旨在向临床医生和其他利益相关者介绍人工智能在预测、诊断和分类轻度和重度神经认知障碍方面的使用、益处和局限性，通过重点介绍探索的特征和使用的 AI 技术，对这一主题进行概念概述。我们介绍了符合以下六个类别的用于这些目的的特征的研究：（1）社会人口统计学；（2）临床和心理计量评估；（3）神经影像学和神经生理学；（4）电子健康记录和索赔；（5）新型评估（例如，数字数据传感器）；以及（6）基因组学/其他组学。对于每个类别，我们提供了 AI 方法的示例，包括监督和无监督的 ML、深度学习和自然语言处理。人工智能技术在医疗保健中仍处于起步阶段，但具有改变我们诊断和治疗神经认知障碍患者的方式的巨大潜力。
