diagnostics Review Artificial Intelligence for Alzheimer’s Disease: Promise or Challenge? Carlo Fabrizio 1 , Andrea Termine 1 , Carlo Caltagirone 2 and Giulia Sancesario 3,4, *   Citation: Fabrizio, C.; Termine, A.; Caltagirone, C.; Sancesario, G. Artificial Intelligence for Alzheimer’s Disease: Promise or Challenge? Diagnostics 2021, 11, 1473. https://doi.org/10.3390/ diagnostics11081473 Academic Editor: Benedetta Nacmias Received: 31 May 2021 Accepted: 12 August 2021 Published: 14 August 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Laboratory of Experimental and Behavioral Neurophysiology, IRCCS Santa Lucia Foundation, 00143 Rome, Italy; c.fabrizio@hsantalucia.it (C.F.); a.termine@hsantalucia.it (A.T.) 2 Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy; c.caltagirone@hsantalucia.it 3 Biobank, IRCCS Santa Lucia Foundation, 00179 Rome, Italy 4 European Center for Brain Research, Experimental Neuroscience, 00143 Rome, Italy * Correspondence: g.sancesario@hsantalucia.it Abstract: Decades of experimental and clinical research have contributed to unraveling many mechanisms in the pathogenesis of Alzheimer’s disease (AD), but the puzzle is still incomplete. Although we can suppose that there is no complete set of puzzle pieces, the recent growth of open data-sharing initiatives collecting lifestyle, clinical, and biological data from AD patients has provided a potentially unlimited amount of information about the disease, far exceeding the human ability to make sense of it. Moreover, integrating Big Data from multi-omics studies provides the potential to explore the pathophysiological mechanisms of the entire biological continuum of AD. In this context, Artificial Intelligence (AI) offers a wide variety of methods to analyze large and complex data in order to improve knowledge in the AD field. In this review, we focus on recent findings and future challenges for AI in AD research. In particular, we discuss the use of Computer-Aided Diagnosis tools for AD diagnosis and the use of AI to potentially support clinical practices for the prediction of individual risk of AD conversion as well as patient stratification in order to finally develop effective and personalized therapies. Keywords: Alzheimer’s disease; diagnosis; machine learning; artificial intelligence 1. Introduction Alzheimer’s disease (AD) is an irreversible neurodegenerative disease that progres- sively destroys cognitive skills, up to the development of dementia. The clinical diagnosis of AD is based on the presence of objective cognitive deficits (which are, typically, prominent memory impairments). In some cases, AD may show atypical presentations, with im- pairments in non-amnesic domains (i.e., attention, executive functions, visuo-constructive practice and language) [1]. However, AD shares many common clinical features with other neurodegenerative dementia, including Lewy body dementia [2], frontotemporal disorders [3], and vascular dementia, making early and differential diagnosis difficult, especially in the first stage of the disease [4,5]. In atypical AD, clinical signs of fluent and non-fluent progressive aphasia, or dysexecutive/behavioral changes, may overlap with frontotemporal dementia syndromes [6]; posterior cortical atrophy (PCA) with underly- ing AD etiology may clinically overlap with dementia with Lewy bodies or corticobasal syndrome [7]. Finally, the occurrence of co-existing pathologies is a common feature in those cases of neurodegenerative diseases that share a common pathogenic mechanism, consisting of extracellular and/or intracellular insoluble fibril aggregates of abnormal misfolded proteins (e.g., the formation of amyloid plaques, tau tangles, or α-synuclein inclusions). In this context, the system biology approach, which aims at the integration of clinical and multi-omics data, can help to detect and recognize the pathophysiological Diagnostics 2021, 11, 1473. https://doi.org/10.3390/diagnostics11081473 https://www.mdpi.com/journal/diagnostics