{"id":2599,"date":"2023-07-17T19:35:31","date_gmt":"2023-07-17T19:35:31","guid":{"rendered":"https:\/\/dailyai.com\/?p=2599"},"modified":"2023-07-17T22:06:29","modified_gmt":"2023-07-17T22:06:29","slug":"google-develops-ai-system-to-enhance-the-accuracy-of-ai-medical-diagnostics","status":"publish","type":"post","link":"https:\/\/dailyai.com\/da\/2023\/07\/google-develops-ai-system-to-enhance-the-accuracy-of-ai-medical-diagnostics\/","title":{"rendered":"Google udvikler system til at forbedre n\u00f8jagtigheden af medicinsk AI-diagnostik"},"content":{"rendered":"<p><b>AI viser sig at v\u00e6re s\u00e6rdeles nyttig til at diagnosticere sygdomme ud fra medicinske billeder, f.eks. r\u00f8ntgenbilleder, i stor skala. Men AI-v\u00e6rkt\u00f8jer er ikke altid i stand til at genkende deres egne un\u00f8jagtigheder.\u00a0<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Som svar har Google udviklet et nyt AI-system kaldet Complementarity-driven Deferral-to-Clinical Workflow (CoDoC), som kan afg\u00f8re, hvorn\u00e5r man skal stole p\u00e5 AI-baserede diagnoser og bede om en second opinion fra en menneskelig l\u00e6ge.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">If\u00f8lge <\/span><a href=\"https:\/\/www.nature.com\/articles\/s41591-023-02437-x\"><span style=\"font-weight: 400;\">Unders\u00f8gelsen<\/span><\/a><span style=\"font-weight: 400;\">CoDoC reducerer arbejdsbyrden ved at analysere medicinske scanningsdata med 66%, men den opdager ogs\u00e5, n\u00e5r en AI-beslutning potentielt er forkert, hvilket reducerer antallet af falske positiver med 25%.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">CoDoC arbejder parallelt med eksisterende AI-systemer, der typisk bruges til at fortolke medicinske billeder som f.eks. r\u00f8ntgenbilleder af brystkassen eller mammografier.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Hvis et AI-v\u00e6rkt\u00f8j f.eks. fortolker et mammogram, vurderer CoDoC, om v\u00e6rkt\u00f8jets opfattede tillid til dets analyse er st\u00e6rk nok til, at man kan stole p\u00e5 det. Hvis der er tvetydighed, beder CoDoC en menneskelig ekspert om en second opinion.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">S\u00e5dan her fungerer det:\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">For at tr\u00e6ne CoDoC tog Google data fra eksisterende kliniske AI-v\u00e6rkt\u00f8jer og sammenlignede dem med en menneskelig klinikers fortolkning af de samme billeder. Modellen blev yderligere valideret med efteranalyse af data via biopsi eller andre metoder.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Denne proces g\u00f8r det muligt for CoDoC at l\u00e6re og forst\u00e5, hvor n\u00f8jagtige et AI-v\u00e6rkt\u00f8js analyser og tillidsniveauer er sammenlignet med menneskelige l\u00e6ger.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">N\u00e5r den er tr\u00e6net, kan CoDoC vurdere, om en AI-analyse af scanninger er trov\u00e6rdig, eller om der er behov for en menneskelig gennemgang.\u00a0<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Alan Karthikesalingam fra Google Health UK, som var involveret i forskningen, <a href=\"https:\/\/www.newscientist.com\/article\/2382756-google-ai-helps-doctors-decide-whether-to-trust-diagnoses-made-by-ai\/\">sagde<\/a>\"Hvis man bruger CoDoC sammen med AI-v\u00e6rkt\u00f8jet og resultaterne fra en rigtig radiolog, og CoDoC derefter hj\u00e6lper med at beslutte, hvilken udtalelse der skal bruges, er den resulterende n\u00f8jagtighed bedre end enten personen eller AI-v\u00e6rkt\u00f8jet alene.\"<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Yderligere test af CoDoC blev udf\u00f8rt ved hj\u00e6lp af forskellige mammografidatas\u00e6t og r\u00f8ntgenbilleder til tuberkulosescreening p\u00e5 tv\u00e6rs af forskellige pr\u00e6diktive AI-systemer, hvilket gav positive resultater.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Krishnamurthy Dvijotham fra Google DeepMind bem\u00e6rkede: \"Fordelen ved CoDoC er, at det er kompatibelt med en r\u00e6kke forskellige propriet\u00e6re AI-systemer.\"<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Helen Salisbury fra University of Oxford p\u00e5peger dog, at nogle medicinske diagnosticeringsprocesser er mere komplekse end dem, CoDoC blev testet med. Hun siger: \"For systemer, hvor du ikke har nogen chance for at p\u00e5virke, hvad der kommer ud af den sorte boks, virker det som en god id\u00e9 at tilf\u00f8je maskinl\u00e6ring. Jeg ved ikke, om det bringer AI, der skal v\u00e6re sammen med os hele dagen, hver dag i vores rutinearbejde, t\u00e6ttere p\u00e5.\"<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Som forskerne fremh\u00e6ver, betyder CoDoC's interoperabilitet, at den kan indg\u00e5 i forskellige diagnostiske arbejdsgange. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">A<\/span><span style=\"font-weight: 400;\">I-systemer kan arbejde sammen med AI-systemer for at forbedre deres n\u00f8jagtighed. Som man siger, ser fire \u00f8jne mere end to. <\/span><\/p>","protected":false},"excerpt":{"rendered":"<p>AI viser sig at v\u00e6re s\u00e6rdeles nyttig til at diagnosticere sygdomme ud fra medicinske billeder, f.eks. r\u00f8ntgenbilleder, i stor skala. Men AI-v\u00e6rkt\u00f8jer er ikke altid i stand til at genkende deres egne un\u00f8jagtigheder.  Som svar har Google udviklet et nyt AI-system kaldet Complementarity-driven Deferral-to-Clinical Workflow (CoDoC), som kan se, hvorn\u00e5r man skal stole p\u00e5 AI-baserede diagnoser og bede om en second opinion fra en menneskelig l\u00e6ge.  If\u00f8lge unders\u00f8gelsen reducerer CoDoC arbejdsbyrden ved at analysere medicinske scanningsdata med 66%, men den opdager ogs\u00e5, n\u00e5r en AI-beslutning potentielt er forkert, hvilket reducerer falske positiver med 25%.  CoDoC arbejder parallelt med eksisterende AI-systemer, der typisk<\/p>","protected":false},"author":2,"featured_media":2600,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[203,147,102,101],"class_list":["post-2599","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-biotech","tag-deepmind","tag-google","tag-medtech"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Google develops system to enhance the accuracy of AI medical diagnostics | DailyAI<\/title>\n<meta name=\"description\" content=\"AI is proving exceptionally useful for diagnosing diseases from medical images, such as X-rays, at scale. However, AI tools aren\u2019t always able to recognize when they could be inaccurate.\u00a0\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dailyai.com\/da\/2023\/07\/google-develops-ai-system-to-enhance-the-accuracy-of-ai-medical-diagnostics\/\" \/>\n<meta property=\"og:locale\" content=\"da_DK\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Google develops system to enhance the accuracy of AI medical diagnostics | DailyAI\" \/>\n<meta property=\"og:description\" content=\"AI is proving exceptionally useful for diagnosing diseases from medical images, such as X-rays, at scale. However, AI tools aren\u2019t always able to recognize when they could be inaccurate.\u00a0\" \/>\n<meta property=\"og:url\" content=\"https:\/\/dailyai.com\/da\/2023\/07\/google-develops-ai-system-to-enhance-the-accuracy-of-ai-medical-diagnostics\/\" \/>\n<meta property=\"og:site_name\" content=\"DailyAI\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-17T19:35:31+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-07-17T22:06:29+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/dailyai.com\/wp-content\/uploads\/2023\/07\/shutterstock_2269953445.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1000\" \/>\n\t<meta property=\"og:image:height\" content=\"667\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Sam Jeans\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DailyAIOfficial\" \/>\n<meta name=\"twitter:site\" content=\"@DailyAIOfficial\" \/>\n<meta name=\"twitter:label1\" content=\"Skrevet af\" \/>\n\t<meta name=\"twitter:data1\" content=\"Sam Jeans\" \/>\n\t<meta name=\"twitter:label2\" content=\"Estimeret l\u00e6setid\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutter\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"NewsArticle\",\"@id\":\"https:\\\/\\\/dailyai.com\\\/2023\\\/07\\\/google-develops-ai-system-to-enhance-the-accuracy-of-ai-medical-diagnostics\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/dailyai.com\\\/2023\\\/07\\\/google-develops-ai-system-to-enhance-the-accuracy-of-ai-medical-diagnostics\\\/\"},\"author\":{\"name\":\"Sam Jeans\",\"@id\":\"https:\\\/\\\/dailyai.com\\\/#\\\/schema\\\/person\\\/711e81f945549438e8bbc579efdeb3c9\"},\"headline\":\"Google develops system to enhance the accuracy of AI medical diagnostics\",\"datePublished\":\"2023-07-17T19:35:31+00:00\",\"dateModified\":\"2023-07-17T22:06:29+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/dailyai.com\\\/2023\\\/07\\\/google-develops-ai-system-to-enhance-the-accuracy-of-ai-medical-diagnostics\\\/\"},\"wordCount\":461,\"publisher\":{\"@id\":\"https:\\\/\\\/dailyai.com\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/dailyai.com\\\/2023\\\/07\\\/google-develops-ai-system-to-enhance-the-accuracy-of-ai-medical-diagnostics\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/dailyai.com\\\/wp-content\\\/uploads\\\/2023\\\/07\\\/shutterstock_2269953445.jpg\",\"keywords\":[\"Biotech\",\"DeepMind\",\"Google\",\"MedTech\"],\"articleSection\":[\"Industry\"],\"inLanguage\":\"da-DK\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/dailyai.com\\\/2023\\\/07\\\/google-develops-ai-system-to-enhance-the-accuracy-of-ai-medical-diagnostics\\\/\",\"url\":\"https:\\\/\\\/dailyai.com\\\/2023\\\/07\\\/google-develops-ai-system-to-enhance-the-accuracy-of-ai-medical-diagnostics\\\/\",\"name\":\"Google develops system to enhance the accuracy of AI medical diagnostics | DailyAI\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/dailyai.com\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/dailyai.com\\\/2023\\\/07\\\/google-develops-ai-system-to-enhance-the-accuracy-of-ai-medical-diagnostics\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/dailyai.com\\\/2023\\\/07\\\/google-develops-ai-system-to-enhance-the-accuracy-of-ai-medical-diagnostics\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/dailyai.com\\\/wp-content\\\/uploads\\\/2023\\\/07\\\/shutterstock_2269953445.jpg\",\"datePublished\":\"2023-07-17T19:35:31+00:00\",\"dateModified\":\"2023-07-17T22:06:29+00:00\",\"description\":\"AI is proving exceptionally useful for diagnosing diseases from medical images, such as X-rays, at scale. 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