[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117764-en":3,"doc-seo-117764-105":30,"detail-sidebar-cat-0-en-105":95},{"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},117764,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Interpretable machine learning for dementia - A systematic review","Automated dementia diagnosis using machine learning has grown rapidly, yet clinical impact remains limited due to the difficulty of building robust, generalizable models that produce decisions clinicians can trust and reliably explain. This systematic review synthesizes interpretable machine learning approaches, including inherently interpretable models and post hoc explainability methods, and highlights variation across validation and reporting while noting heavy reliance on common datasets.","Received: 14 September 2022 Revised: 5 December 2022 Accepted: 20 December 2022  \nDOI: 10.1002/alz.12948  \nREVIEW ARTICLE  \nInterpretable machine learning for dementia: A systematic review  \nSophie A. Martin1, 2   Florence J. Townend1   Frederik Barkhof1, 2, 3   James H. Cole1, 2   \n1 Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK  \n2 Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, UK  \n3Amsterdam UMC, Department of Radiology & Nuclear Medicine, Vrije Universiteit, Amsterdam, Netherlands  \nCorrespondence  \nSophie A. Martin, Centre for Medical Image Computing, Department of Computer Science, University College London, 90 High Holborn, London, WC1V 6LJ, UK. [Email: s.martin.20@ucl.ac.uk](Email: s.martin.20@ucl.ac.uk)  \nFunding information  \nEngineering and Physical Sciences Research Council, Grant/Award Number: EP/S021930/1  \nAbstract  \nIntroduction: Machine learning research into automated dementia diagnosis is becoming increasingly popular but so far has had limited clinical impact. A key challenge is building robust and generalizable models that generate decisions that can be reliably explained. Some models are designed to be inherently “interpretable,”whereas post hoc “explainability” methods can be used for other models.  \nMethods: Here we sought to summarize the state-of-the-art of interpretable machine learning for dementia.  \nResults: We identified 92 studies using PubMed, Web of Science, and Scopus. Studies demonstrate promising classification performance but vary in their validation procedures and reporting standards and rely heavily on popular data sets.  \nDiscussion: Future work should incorporate clinicians to validate explanation methods and make conclusive inferences about dementia-related disease pathology. Critically analyzing model explanations also requires an understanding of the interpretability methods itself. Patient-specific explanations are also required to demonstrate the benefit of interpretable machine learning in clinical practice.  \nKEYWORDS  \ndementia, diagnosis, explainable artificial intelligence, interpretability, machine learning, mild cognitive impairment  \n1  INTRODUCTION  \nTraditional dementia diagnosis typically relies on longitudinal clinical observations, medical history, and symptoms of cognitive decline such as impaired memory and visuospatial deficits, often supported by imaging findings. Computer-aided decision tools are increasingly making use of machine learning to speed up diagnosis, provide support where expert knowledge is sparce, and reduce subjectivity.1 Machine learn-  \ning models have been shown to perform as well as, or even exceed the accuracy of predictions made from imaging by radiologists, as they can exploit the rich information present in dense, high-dimensional data.2 They also show promise at identifying those at risk earlier in the disease trajectory, because relying on longitudinal clinical observations usually means that the disease has already progressed beyond the point that preventive protocols or adjustmentscan be effective. However, despite promising results in medical research, computer-aided tools have yet to  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \n© 2023 The Authors. Alzheimer’s & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer’s Association.  \nAlzheimer’s Dement. 2023;1–15. wi[leyonlinelibrary.com/journal/alz](leyonlinelibrary.com/journal/alz)  1  \n15525279, 0, Downloaded from [https://alz-journals.onlinelibrary.wiley.com/doi/10.1002/alz.12948 by University College London UCL Library Services, Wiley Online Library on [08/02/2023]. See the Terms and Conditions (](https://alz-journals.onlinelibrary.wiley.com/doi/10.1002/alz.12948 by University College London UCL Librar","cbCaicM0VkCHRRH6","https://ap.wps.com/l/cbCaicM0VkCHRRH6","pdf",1910849,1,15,"English","en",105,"# Introduction\n## Interpretable machine learning and explainability in dementia diagnosis\n# Methods\n## Systematic review approach\n# Results\n## Study selection and evaluation findings\n# Discussion\n## Future directions and clinical translation","[{\"question\":\"这篇系统综述关注的核心问题是什么？\",\"answer\":\"重点在于总结用于痴呆的可解释机器学习（IML）研究现状，以及如何让模型输出能够被可靠解释并提升临床可信度。\"},{\"question\":\"可解释机器学习在文中主要包含哪些思路？\",\"answer\":\"既包括“内在可解释”的模型，也包括“事后可解释”的解释性（XAI）方法，通过机制说明、特征重要性强调或为特定结局生成样例来解释模型决策。\"},{\"question\":\"研究结果显示当前证据有哪些主要不足？\",\"answer\":\"尽管分类性能有前景，但研究在验证流程与报告标准上差异较大，且对流行数据集的依赖较重。\"},{\"question\":\"文中对未来工作提出了哪些关键方向？\",\"answer\":\"建议纳入临床医生验证解释方法，并从模型解释中做出更具结论性的推断；同时需要理解解释方法本身，并提供面向患者的解释以证明其在临床实践中的价值。\"}]","Interpretable machine learning for dementia - A systematic review | PDF",1785679443,38,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"interpretable-machine-learning-for-dementia-a-systematic-review","",{"@graph":36,"@context":89},[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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/interpretable-machine-learning-for-dementia-a-systematic-review/117764/",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","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"这篇系统综述关注的核心问题是什么？","Question",{"text":75,"@type":76},"重点在于总结用于痴呆的可解释机器学习（IML）研究现状，以及如何让模型输出能够被可靠解释并提升临床可信度。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"可解释机器学习在文中主要包含哪些思路？",{"text":80,"@type":76},"既包括“内在可解释”的模型，也包括“事后可解释”的解释性（XAI）方法，通过机制说明、特征重要性强调或为特定结局生成样例来解释模型决策。",{"name":82,"@type":73,"acceptedAnswer":83},"研究结果显示当前证据有哪些主要不足？",{"text":84,"@type":76},"尽管分类性能有前景，但研究在验证流程与报告标准上差异较大，且对流行数据集的依赖较重。",{"name":86,"@type":73,"acceptedAnswer":87},"文中对未来工作提出了哪些关键方向？",{"text":88,"@type":76},"建议纳入临床医生验证解释方法，并从模型解释中做出更具结论性的推断；同时需要理解解释方法本身，并提供面向患者的解释以证明其在临床实践中的价值。","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":110,"slug":142},19,"General","general"]