{"id":221,"date":"2024-04-18T10:36:00","date_gmt":"2024-04-18T09:36:00","guid":{"rendered":"https:\/\/jingcs.com\/?p=221"},"modified":"2024-04-20T04:58:30","modified_gmt":"2024-04-20T03:58:30","slug":"ei-an-interactive-xai-interface-with-application-in-healthcare-for-non-experts","status":"publish","type":"post","link":"https:\/\/jingcs.com\/index.php\/ei-an-interactive-xai-interface-with-application-in-healthcare-for-non-experts\/","title":{"rendered":"[EI] An Interactive XAI Interface with\u00a0Application in\u00a0Healthcare for\u00a0Non-experts"},"content":{"rendered":"\n<h2>Abstract<\/h2>\n\n\n\n<p>Explainable artificial intelligence (XAI) has gained increasing attention in the medical field, where understanding the reasons for predictions is crucial. In this paper we introduce an interactive and dynamic visual interface providing global, local and counterfactual explanations to end-users, with a use case in healthcare. The dataset used in the study is about predicting an individual\u2019s coronary heart disease (CHD) within 10 years using the decision tree classification method. We evaluated our XAI system with 200 participants. Our results show that the participants reported an overall good assessment of the user interface, with non-expert users showing a higher satisfaction than users who have some degree of knoweldge of AI.<\/p>\n\n\n\n<p>Paper PDF Link: <a href=\"https:\/\/research-information.bris.ac.uk\/ws\/portalfiles\/portal\/379782666\/XAI2023Hu.pdf\">https:\/\/research-information.bris.ac.uk\/ws\/portalfiles\/portal\/379782666\/XAI2023Hu.pdf<\/a><\/p>\n\n\n\n<p>Demo Link: <a href=\"http:\/\/med.bristol-xai.com\">med.bristol-xai.com <\/a><\/p>\n\n\n\n<p>Demo Link (backup): <a href=\"http:\/\/med-xai.jingcs.com\" target=\"_blank\" rel=\"noreferrer noopener\">med-xai.jingcs.com<\/a><\/p>\n\n\n\n<p><\/p>\n\n\n\n<p>*Counterfactual generation demonstrations are temporarily disabled in the interface due to server budget limits. Our implementation is based on DiCE, and <a href=\"https:\/\/github.com\/interpretml\/DiCE\/blob\/main\/docs\/source\/notebooks\/DiCE_getting_started.ipynb\">here<\/a> provides a detailed introduction and user-friendly examples of explanations generated by DiCE.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<h2>Ref<\/h2>\n\n\n\n<pre class=\"wp-block-preformatted\">@inproceedings{hu2023interactive,\n  title={An Interactive XAI Interface with Application in Healthcare for Non-experts},\n  author={Hu, Jingyu and Liang, Yizhu and Zhao, Weiyu and McAreavey, Kevin and Liu, Weiru},\n  booktitle={World Conference on Explainable Artificial Intelligence},\n  pages={649--670},\n  year={2023},\n  organization={Springer}\n}<\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Abstract Explainable artificial intelligence (XAI) has gained increasing attention in the medical field, where understanding the reasons for predictions is crucial. In this paper we introduce an interactive and dynamic visual interface providing global, local and counterfactual explanations to end-users, with a use case in healthcare. The dataset used in the study is about predicting<\/p><\/div>\n<div class=\"blog-btn\"><a href=\"https:\/\/jingcs.com\/index.php\/ei-an-interactive-xai-interface-with-application-in-healthcare-for-non-experts\/\" class=\"home-blog-btn\">Read More<\/a><\/p>\n","protected":false},"author":1,"featured_media":226,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[34,28],"tags":[71,72,24,70],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v19.10 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>[EI] An Interactive XAI Interface with\u00a0Application in\u00a0Healthcare for\u00a0Non-experts - Jingyu\u2018s Blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/jingcs.com\/index.php\/ei-an-interactive-xai-interface-with-application-in-healthcare-for-non-experts\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"[EI] An Interactive XAI Interface with\u00a0Application in\u00a0Healthcare for\u00a0Non-experts - Jingyu\u2018s Blog\" \/>\n<meta property=\"og:description\" content=\"Abstract Explainable artificial intelligence (XAI) has gained increasing attention in the medical field, where understanding the reasons for predictions is crucial. 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