{"id":16539,"date":"2024-09-20T11:55:09","date_gmt":"2024-09-20T09:55:09","guid":{"rendered":"https:\/\/is.ijs.si\/?p=16539"},"modified":"2025-03-26T14:23:03","modified_gmt":"2025-03-26T13:23:03","slug":"artificial-intelligence-augmented-systems-in-healthcare-using-deep-learning-algorithms","status":"publish","type":"post","link":"https:\/\/is.ijs.si\/?p=16539","title":{"rendered":"Artificial Intelligence Augmented Systems in Healthcare Using Deep Learning Algorithms"},"content":{"rendered":"\n<p>Kennedy Addo<\/p>\n<p><strong>Abstract<\/strong><br \/>This study assesses the impact of AI, specifically deep<br \/>learning algorithms, on diagnostic accuracy. It identifies<br \/>various diseases where AI is applied, such as cancer,<br \/>cardiovascular diseases, and neurological disorders. By<br \/>analyzing existing AI implementations, the research<br \/>highlights improvements in early detection and diagnostic<br \/>precision. Additionally, the study proposes a novel deep<br \/>learning model to enhance clinicians\u2019 decision-making at<br \/>the point of care. This framework aims to integrate<br \/>seamlessly with clinical workflows, providing real-time<br \/>insights and recommendations. The proposed model<br \/>focuses on improving patient outcomes through accurate,<br \/>timely, and data-driven clinical decisions.<\/p>\n<p>\u00a0<\/p>\n\n\n\n<div data-wp-interactive=\"core\/file\" class=\"wp-block-file\"><object data-wp-bind--hidden=\"!state.hasPdfPreview\" hidden class=\"wp-block-file__embed\" data=\"https:\/\/is.ijs.si\/wp-content\/uploads\/2024\/10\/IS2024_-_CHATGPT_in_MEDICINE_paper_4-1.pdf\" type=\"application\/pdf\" style=\"width:100%;height:600px\" aria-label=\"Embed of IS2024_-_CHATGPT_in_MEDICINE_paper_4-1.\"><\/object><a id=\"wp-block-file--media-12752a4c-8dc6-43a9-bff7-af52428853b1\" href=\"https:\/\/is.ijs.si\/wp-content\/uploads\/2024\/10\/IS2024_-_CHATGPT_in_MEDICINE_paper_4-1.pdf\">IS2024_-_CHATGPT_in_MEDICINE_paper_4-1<\/a><a href=\"https:\/\/is.ijs.si\/wp-content\/uploads\/2024\/10\/IS2024_-_CHATGPT_in_MEDICINE_paper_4-1.pdf\" class=\"wp-block-file__button wp-element-button\" download aria-describedby=\"wp-block-file--media-12752a4c-8dc6-43a9-bff7-af52428853b1\">Download<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":29,"featured_media":24966,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[117,102],"tags":[],"class_list":["post-16539","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-doi-chat-2024","category-papers"],"_links":{"self":[{"href":"https:\/\/is.ijs.si\/index.php?rest_route=\/wp\/v2\/posts\/16539","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/is.ijs.si\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/is.ijs.si\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/is.ijs.si\/index.php?rest_route=\/wp\/v2\/users\/29"}],"replies":[{"embeddable":true,"href":"https:\/\/is.ijs.si\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=16539"}],"version-history":[{"count":3,"href":"https:\/\/is.ijs.si\/index.php?rest_route=\/wp\/v2\/posts\/16539\/revisions"}],"predecessor-version":[{"id":25088,"href":"https:\/\/is.ijs.si\/index.php?rest_route=\/wp\/v2\/posts\/16539\/revisions\/25088"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/is.ijs.si\/index.php?rest_route=\/wp\/v2\/media\/24966"}],"wp:attachment":[{"href":"https:\/\/is.ijs.si\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=16539"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/is.ijs.si\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=16539"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/is.ijs.si\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=16539"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}