[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122484-en":3,"doc-seo-122484-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122484,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",7,"Healthcare","Harnessing the potential of machine learning and artificial intelligence for dementia research","Progress in dementia research remains limited due to major gaps in identifying prevention targets, clarifying disease progression mechanisms, and delivering disease-modifying therapies. At the same time, the increasing availability of multimodal datasets enables machine learning and artificial intelligence to address key questions across genetics, experimental medicine, drug discovery, and trial optimisation. The article reviews opportunities and challenges, including genetic enhancement, multimodal severity and subtype characterisation, neuroimaging performance, and risk-factor causality for improved prediction and preventative interventions.","Ranson et al. Brain Informatics (2023) 10:6 [https://doi.org/10.1186/s40708-022-00183-3](https://doi.org/10.1186/s40708-022-00183-3)  \nBrain Informatics  \n REVIEW Open Access  \nHarnessing the potential of machine  \nlearning and artificial intelligence for dementia research  \nJanice M. Ranson 1*, Magda Bucholc2, Donald Lyall3, Danielle Newby4, Laura Winchester4, Neil P. Oxtoby5, Michele Veldsman6, Timothy Rittman7, Sarah Marzi8,9, Nathan Skene8,9, Ahmad Al Khleifat 10, Isabelle F. Foote 11, Vasiliki Orgeta12, Andrey Kormilitzin3, Ilianna Lourida 1 and David J. Llewellyn 1,13  \nAbstract  \nProgress in dementia research has been limited, with substantial gaps in our knowledge of targets for prevention, mechanisms for disease progression, and disease-modifying treatments. The growing availability of multimodal datasets opens possibilities for the application of machine learning and artificial intelligence (AI) to help answer key questions in the field. We provide an overview of the state of the science, highlighting current challenges and opportunities for utilisation of AI approaches to move the field forward in the areas of genetics, experimental medicine, drug discovery and trials optimisation, imaging, and prevention. Machine learning methods can enhance results of genetic studies, help determine biological effects and facilitate the identification of drug targets based on genetic and transcriptomic information. The use of unsupervised learning for understanding disease mechanisms for drug discovery is promising, while analysis of multimodal data sets to characterise and quantify disease severity and subtype are also beginning to contribute to optimisation of clinical trial recruitment. Data-driven experimental medicine is needed to analyse data across modalities and develop novel algorithms to translate insights from animal models to human disease biology. AI methods in neuroimaging outperform traditional approaches for diagnostic classification, and although challenges around validation and translation remain, there is optimism for their meaningful integration to clinical practice in the near future. AI-based models can also clarify our understanding of the causality and commonality of dementia risk factors, informing and improving risk prediction models along with the development of preventative interventions. The complexity and heterogeneity of dementia requires an alternative approach beyond traditional design and analytical approaches. Although not yet widely used in dementia research, machine learning and AI have the potential to unlock current challenges and advance precision dementia medicine.  \nKeywords Dementia, Artificial intelligence, Machine learning, Genetics, Drug discovery, Neuroimaging, Prevention, iPSC, Animal models  \n*Correspondence: Janice M. Ranson  \n[J.Ranson@exeter.ac.uk](J.Ranson@exeter.ac.uk)  \n1 University of Exeter Medical School, College House, St Luke’s Campus, Heavitree Road, Exeter EX1 2LU, UK  \n2 Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, UK  \n3 Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK  \n4 Department of Psychiatry, University of Oxford, Oxford, UK  \n5 Department of Computer Science, UCL Centre for Medical Image Computing, University College London, London, UK  \n6 Cambridge Cognition, Cambridge, UK  \n7 Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK  \n8 UK Dementia Research Institute, Imperial College London, London, UK  \n9 Department of Brain Sciences, Imperial College London, London, UK  \n10 Department of Basic and Clinical Neuroscience, King’s College London, London, UK  \n11 University of Colorado Boulder, Boulder, USA  \n12 Division of Psychiatry, University College London, London, UK  \n13 The Alan Turing Institute, London, UK  \n© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits ","cbCaimSiZmSgeVnk","https://ap.wps.com/l/cbCaimSiZmSgeVnk","pdf",866245,1,12,"English","en",105,"# Abstract\n# Introduction\n# Key Applications of Machine Learning and AI in Dementia Research\n# Challenges and Opportunities\n# Outlook for Precision Dementia Medicine","[{\"question\":\"What gaps in dementia research motivate the use of machine learning and AI?\",\"answer\":\"Key gaps include limited knowledge of prevention targets, uncertainty about disease progression mechanisms, and the lack of disease-modifying treatments. These constraints motivate data-driven approaches to accelerate discovery.\"},{\"question\":\"How can machine learning improve genetic and drug discovery workflows in dementia research?\",\"answer\":\"Machine learning can enhance genetic study results, identify biological effects, and facilitate drug target identification using genetic and transcriptomic information. Unsupervised learning for uncovering disease mechanisms also supports drug discovery.\"},{\"question\":\"What role do AI methods in neuroimaging play, and what limitations remain?\",\"answer\":\"AI methods in neuroimaging can outperform traditional approaches for diagnostic classification. Validation and translation challenges remain, but integration into clinical practice is considered promising.\"}]","Harnessing the potential of machine learning and artificial intelligence for dementia research | PDF",1785810900,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"harnessing-the-potential-of-machine-learning-and-artificial-intelligence-for-dementia-research","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/harnessing-the-potential-of-machine-learning-and-artificial-intelligence-for-dementia-research/122484/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What gaps in dementia research motivate the use of machine learning and AI?","Question",{"text":75,"@type":76},"Key gaps include limited knowledge of prevention targets, uncertainty about disease progression mechanisms, and the lack of disease-modifying treatments. These constraints motivate data-driven approaches to accelerate discovery.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How can machine learning improve genetic and drug discovery workflows in dementia research?",{"text":80,"@type":76},"Machine learning can enhance genetic study results, identify biological effects, and facilitate drug target identification using genetic and transcriptomic information. Unsupervised learning for uncovering disease mechanisms also supports drug discovery.",{"name":82,"@type":73,"acceptedAnswer":83},"What role do AI methods in neuroimaging play, and what limitations remain?",{"text":84,"@type":76},"AI methods in neuroimaging can outperform traditional approaches for diagnostic classification. 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