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Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease

Clinical trials for Alzheimer’s disease (AD) face multiple challenges, such as the high screen failure rate and the even allocation of heterogeneous participants. Artificial intelligence (AI), which has become a potent tool of modern science with the expansion in the volume, variety, and velocity of...

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Detalles Bibliográficos
Autores principales: Seo, Younghoon, Jang, Hyemin, Lee, Hyejoo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8879055/
https://www.ncbi.nlm.nih.gov/pubmed/35207561
http://dx.doi.org/10.3390/life12020275
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author Seo, Younghoon
Jang, Hyemin
Lee, Hyejoo
author_facet Seo, Younghoon
Jang, Hyemin
Lee, Hyejoo
author_sort Seo, Younghoon
collection PubMed
description Clinical trials for Alzheimer’s disease (AD) face multiple challenges, such as the high screen failure rate and the even allocation of heterogeneous participants. Artificial intelligence (AI), which has become a potent tool of modern science with the expansion in the volume, variety, and velocity of biological data, offers promising potential to address these issues in AD clinical trials. In this review, we introduce the current status of AD clinical trials and the topic of machine learning. Then, a comprehensive review is focused on the potential applications of AI in the steps of AD clinical trials, including the prediction of protein and MRI AD biomarkers in the prescreening process during eligibility assessment and the likelihood stratification of AD subjects into rapid and slow progressors in randomization. Finally, this review provides challenges, developments, and the future outlook on the integration of AI into AD clinical trials.
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spelling pubmed-88790552022-02-26 Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease Seo, Younghoon Jang, Hyemin Lee, Hyejoo Life (Basel) Review Clinical trials for Alzheimer’s disease (AD) face multiple challenges, such as the high screen failure rate and the even allocation of heterogeneous participants. Artificial intelligence (AI), which has become a potent tool of modern science with the expansion in the volume, variety, and velocity of biological data, offers promising potential to address these issues in AD clinical trials. In this review, we introduce the current status of AD clinical trials and the topic of machine learning. Then, a comprehensive review is focused on the potential applications of AI in the steps of AD clinical trials, including the prediction of protein and MRI AD biomarkers in the prescreening process during eligibility assessment and the likelihood stratification of AD subjects into rapid and slow progressors in randomization. Finally, this review provides challenges, developments, and the future outlook on the integration of AI into AD clinical trials. MDPI 2022-02-12 /pmc/articles/PMC8879055/ /pubmed/35207561 http://dx.doi.org/10.3390/life12020275 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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/).
spellingShingle Review
Seo, Younghoon
Jang, Hyemin
Lee, Hyejoo
Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease
title Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease
title_full Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease
title_fullStr Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease
title_full_unstemmed Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease
title_short Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease
title_sort potential applications of artificial intelligence in clinical trials for alzheimer’s disease
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8879055/
https://www.ncbi.nlm.nih.gov/pubmed/35207561
http://dx.doi.org/10.3390/life12020275
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