{"id":16680,"date":"2024-09-24T09:39:55","date_gmt":"2024-09-24T07:39:55","guid":{"rendered":"https:\/\/is.ijs.si\/?p=16680"},"modified":"2024-10-02T09:34:20","modified_gmt":"2024-10-02T07:34:20","slug":"standards-for-the-use-of-large-language-models-medical-diagnosis","status":"publish","type":"post","link":"https:\/\/is.ijs.si\/?p=16680","title":{"rendered":"Standards for the use of Large Language Models medical diagnosis"},"content":{"rendered":"\n<p>Mihailo Svetozarevic, Isidora Jankovic and Sonja Jankovic<\/p>\n<p>Introduction<br \/>Artificial intelligence (AI), by definition and in the broadest terms, represents intelligence<br \/>exhibited by computer systems. The main goal of AI is to enable computers and machines to<br \/>mimic human cognitive functions. In other words, it aims to simulate human learning,<br \/>comprehension, problem-solving, and critical decision-making. AI approaches human<br \/>cognition in two distinct ways: the symbolic and the connectionist approaches [Esteva et al.,<br \/>2019]. The symbolic approach seeks to replicate human intelligence by analyzing cognition<br \/>independently of the biological structure of the central nervous system, while the connectionist<br \/>approach aims to create neural networks that imitate the brain\u2019s structure. To fully harness the<br \/>potential of AI in healthcare, a systematic approach to its evaluation and benchmarking is<br \/>crucial to ensure that AI becomes a positive contributor to health systems.<\/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_9-2.pdf\" type=\"application\/pdf\" style=\"width:100%;height:600px\" aria-label=\"Embed of IS2024_-_CHATGPT_in_MEDICINE_paper_9-2.\"><\/object><a id=\"wp-block-file--media-43886ebe-0074-4106-8da1-7ed9f6718dbe\" href=\"https:\/\/is.ijs.si\/wp-content\/uploads\/2024\/10\/IS2024_-_CHATGPT_in_MEDICINE_paper_9-2.pdf\">IS2024_-_CHATGPT_in_MEDICINE_paper_9-2<\/a><a href=\"https:\/\/is.ijs.si\/wp-content\/uploads\/2024\/10\/IS2024_-_CHATGPT_in_MEDICINE_paper_9-2.pdf\" class=\"wp-block-file__button wp-element-button\" download aria-describedby=\"wp-block-file--media-43886ebe-0074-4106-8da1-7ed9f6718dbe\">Download<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":29,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[102],"tags":[],"class_list":["post-16680","post","type-post","status-publish","format-standard","hentry","category-papers"],"_links":{"self":[{"href":"https:\/\/is.ijs.si\/index.php?rest_route=\/wp\/v2\/posts\/16680","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=16680"}],"version-history":[{"count":2,"href":"https:\/\/is.ijs.si\/index.php?rest_route=\/wp\/v2\/posts\/16680\/revisions"}],"predecessor-version":[{"id":16914,"href":"https:\/\/is.ijs.si\/index.php?rest_route=\/wp\/v2\/posts\/16680\/revisions\/16914"}],"wp:attachment":[{"href":"https:\/\/is.ijs.si\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=16680"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/is.ijs.si\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=16680"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/is.ijs.si\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=16680"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}