{"id":10692,"date":"2021-11-29T08:20:56","date_gmt":"2021-11-29T08:20:56","guid":{"rendered":"https:\/\/direc.dk\/?p=10692"},"modified":"2025-11-04T12:42:08","modified_gmt":"2025-11-04T12:42:08","slug":"forklarlig-kunstig-intelligens-skal-oege-sygehuses-brug-af-ai","status":"publish","type":"post","link":"https:\/\/direc.dk\/da\/forklarlig-kunstig-intelligens-skal-oege-sygehuses-brug-af-ai\/","title":{"rendered":"Forklarlig kunstig intelligens skal \u00f8ge sygehuses brug af AI"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tTil nyheder<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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26. november 2021<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Forklarlig kunstig intelligens skal \u00f8ge sygehuses brug af AI <\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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I nyt DIREC-projekt samarbejder AI-forskere med sygehuse om at skabe mere nyttig AI og AI-algoritmer, der er lettere at forst\u00e5.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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AI (kunstig intelligens) vinder gradvist st\u00f8rre plads i medicinske hj\u00e6lpeteknologier s\u00e5som billedbaseret diagnose, hvor den kunstige intelligens med overmenneskelig pr\u00e6cision kan analysere skanningsbilleder. AI er derimod sj\u00e6ldent designet som en samarbejdspartner for l\u00e6gepersonalet.<\/p>\n

I et nyt human-AI-projekt EXPLAIN-ME <\/a> – st\u00f8ttet af det nationale forskningscenter DIREC vil AI-forskere sammen med l\u00e6ger udvikle forklarlig kunstig intelligens (Explainable AI – XAI), der kan give klinikere feedback, n\u00e5r de uddanner sig p\u00e5 hospitalernes tr\u00e6ningsklinikker.<\/p>\n

\u201dI den vestlige verden vurderes omkring hver tiende diagnose at v\u00e6re forkert, s\u00e5 patienterne ikke f\u00e5r den rette behandling. Forklaringen kan skyldes manglende erfaring og tr\u00e6ning. Vores XAI-model vil hj\u00e6lpe l\u00e6gepersonalet med at tr\u00e6ffe beslutninger og virke lidt som en mentor, der giver r\u00e5d og respons, n\u00e5r de tr\u00e6ner,\u201d forklarer Aasa Feragen, projektleder og professor p\u00e5 DTU Compute.<\/p>\n

I projektet samarbejder DTU, K\u00f8benhavns Universitet, Aalborg Universitet og Roskilde Universitet med l\u00e6ger p\u00e5 tr\u00e6nings- og simulationscentret CAMES p\u00e5 Rigshospitalet, NordSim p\u00e5 Aalborg Universitetshospital samt kr\u00e6ftl\u00e6ger p\u00e5 Urologisk Afdeling p\u00e5 Sj\u00e6llands Universitetshospital i Roskilde.<\/p>\n

Ultralydsskanning af gravide \u2028<\/strong><\/p>\n

P\u00e5 CAMES vil DTU og K\u00f8benhavns Universitet udvikle en XAI-model, der kigger l\u00e6ger og jordem\u00f8dre over skulderen, n\u00e5r de i tr\u00e6ningsklinikken ultralydsskanner \u2019gravide\u2019 tr\u00e6ningsdukker.<\/p>\n

Inden for ultralydsskanning arbejder klinikere ud fra specifikke \u2019standardplaner\u2019, som viser forskellige dele af fostrets anatomi, s\u00e5 man kan reagere ved komplikationer. Reglerne bliver implementeret i XAI-modellen, der bliver integreret i en simulator, s\u00e5 l\u00e6gen f\u00e5r feedback undervejs.<\/p>\n

Forskerne tr\u00e6ner den kunstige intelligens p\u00e5 rigtige data fra Rigshospitalets ultralydsskanninger fra 2009 til 2018, og det er prim\u00e6rt billeder fra de almindelige nakkefolds- og misdannelsesskanninger, som alle gravide tilbydes cirka 12 og 20 uger inde i graviditeten. N\u00e5r XAI-modellerne om godt et \u00e5rs tid vil blive anvendt p\u00e5 tr\u00e6ningsklinikken, skal man f\u00f8rst tjekke, om modellen ogs\u00e5 virker i simulatoren, eftersom EAI-modellen er tr\u00e6net p\u00e5 rigtige data, mens tr\u00e6ningsdukken er kunstige data.<\/p>\n

If\u00f8lge l\u00e6ger afh\u00e6nger kvaliteten af ultralydsskanninger og evnen til at stille rette diagnoser af, hvor megen tr\u00e6ning l\u00e6gerne har f\u00e5et.<\/p>\n

\u201dHvis vores model undervejs kan fort\u00e6lle l\u00e6gen, at der mangler en fod i billedet for, at billedet er godt nok, vil l\u00e6gen muligvis kunne l\u00e6re hurtigere. Hvis vi ogs\u00e5 kan f\u00e5 XAI-modellen til at fort\u00e6lle, at sonden p\u00e5 ultralydsapparatet skal flyttes lidt for at f\u00e5 alt med i billedet, s\u00e5 kan det m\u00e5ske anvendes i l\u00e6gepraksis ogs\u00e5. Det ville v\u00e6re fantastisk, hvis XAI ogs\u00e5 kan hj\u00e6lpe mindre tr\u00e6nede l\u00e6ger til at lave skanninger, der er p\u00e5 h\u00f8jde med de meget tr\u00e6nede l\u00e6ger,\u201d siger Aasa Feragen.<\/p>\n

Forskningslektor og leder af CAMES\u2019 forskningsgruppe inden for kunstig intelligens Martin Gr\u00f8nneb\u00e6k Tolsgaard understreger, at mange l\u00e6ger er interesserede i at f\u00e5 hj\u00e6lp af AI-teknologien til at fastl\u00e6gge den bedste behandling for patienterne. Og her er forklarlig AI vejen frem.<\/p>\n

\u201dMange af de AI-modeller, der findes i dag, giver ikke s\u00e6rlig god indsigt i, hvorfor de kommer frem til en bestemt beslutning. Det er vigtigt for os at blive klogere p\u00e5. Hvis man ikke forklarer, hvorfor den kunstige intelligens kommer frem til en given beslutning, s\u00e5 tror klinikerne ikke p\u00e5 beslutningen. S\u00e5 hvis man vil bruge AI til g\u00f8re klinikerne bedre, s\u00e5 er det ikke nok bare at f\u00e5 beslutningerne, men ogs\u00e5 behov for gode forklaringer, det vil sige Explainable AI.”<\/p>\n

L\u00f8bende feedback ved robotkirurgi \u2028<\/strong><\/p>\n

Robotkirurgi giver kirurger mulighed for at udf\u00f8re deres arbejde med mere pr\u00e6cision og kontrol end traditionelle kirurgiske v\u00e6rkt\u00f8jer. Det reducerer fejl og \u00f8ger effektiviteten, og forventningen er, at AI vil kunne forbedre resultaterne yderligere.<\/p>\n

I Aalborg skal forskerne udvikle en XAI-model, der st\u00f8tter l\u00e6gerne i tr\u00e6ningscentret NordSim, hvor b\u00e5de danske og udenlandske l\u00e6ger kan tr\u00e6ne kirurgi og operationer i robotsimulatorer med f.eks. grisehjerter. Modellen skal give l\u00f8bende feedback til klinikerne, imens de tr\u00e6ner en operation, og uden at det forstyrrer, fort\u00e6ller Mikael B. Skov, professor p\u00e5 Department of Computer Science ved AAU:<\/p>\n

\u201dI dag er det typisk s\u00e5dan, at man f\u00f8rst f\u00e5r at vide, hvis man skulle have gjort noget anderledes, n\u00e5r man er f\u00e6rdig med at tr\u00e6ne en operation. Vi vil gerne se p\u00e5, hvordan man kan komme med den her feedback mere kontinuerligt, s\u00e5ledes, at man bedre forst\u00e5r, om man har gjort noget rigtigt eller forkert. Det skal helst g\u00f8res s\u00e5dan, at personerne l\u00e6rer det hurtigere og samtidig beg\u00e5r f\u00e6rre fejl, inden de skal ud og lave rigtige operationer. Vi skal derfor se p\u00e5, hvordan man kan komme med forskellige typer af feedback, som f.eks. advarsler, uden at det afbryder for meget\u201d.<\/p>\n

Billedanalyser ved nyrekr\u00e6ft\u2028<\/strong><\/p>\n

L\u00e6ger skal ofte tr\u00e6ffe beslutninger under tidspres, f.eks. i forbindelse med kr\u00e6ftdiagnoser, fordi man vil undg\u00e5, at kr\u00e6ften spreder sig. En falsk positiv diagnose kan derfor betyde, at patienten f\u00e5r fjernet en rask nyre og p\u00e5f\u00f8res andre komplikationer. Selv om erfaringen viser, at AI-metoder er mere pr\u00e6cise i vurderingerne end l\u00e6gerne, har l\u00e6gerne brug for en god forklaring p\u00e5, hvorfor de matematiske modeller klassificerer en tumor som godartet eller ondartet.<\/p>\n

I DIREC-projektet vil forskere fra Roskilde Universitet udvikle metoder, hvor kunstig intelligens analyserer medicinske billeder til brug ved diagnosticering af nyrekr\u00e6ft. L\u00e6gerne vil hj\u00e6lpe dem med at forst\u00e5, hvilken feedback der er brug for fra AI-modellerne, s\u00e5 man finder en balance mellem, hvad der er teknisk muligt, og hvad der er klinisk n\u00f8dvendigt.<\/p>\n

\u201dDet er vigtigt, at teknologien skal kunne indg\u00e5 i hospitalernes praksis, og derfor har vi is\u00e6r fokus p\u00e5 at designe de her metoder inden for \u2019Explainable AI\u2019 i direkte samarbejde med de l\u00e6ger, der rent faktisk skal bruge den i deres beslutningstagning. Her tr\u00e6kker vi is\u00e6r p\u00e5 vores ekspertise inden for Participatory Design, som er en systematisk tilgang til at opn\u00e5 den bedste synergi mellem, hvad AI-forskeren kan komme op med af teknologiske innovationer, og hvad l\u00e6gerne har brug for,\u201d siger Henning Christiansen, professor i datalogi p\u00e5 Institut for Mennesker og Teknologi p\u00e5 Roskilde Universitet.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Om DIREC \u2013 Digital Research Centre Denmark<\/h6>\n

Det nationale forskningscenter DIREC skal s\u00e6tte Danmark i front med de nyeste digitale teknologier gennem digital forskning i verdensklasse. For at tilfredsstille det store behov for h\u00f8jtuddannede it-specialister hj\u00e6lper DIREC derudover med at udbygge kapaciteten inden for b\u00e5de forskning og uddannelse af dataloger. Centeret har et samlet budget p\u00e5 275 millioner og er st\u00f8ttet af Innovationsfonden med 100 millioner. Partnerkredsen bag er et unikt samarbejde p\u00e5 tv\u00e6rs af de datalogiske institutter p\u00e5 landets otte universiteter og Alexandra Instituttet. <\/p>\n

Centerets aktiviteter sker p\u00e5 baggrund af samfundsm\u00e6ssige behov, hvor forskning l\u00f8bende oms\u00e6ttes til v\u00e6rdiskabende l\u00f8sninger i samarbejde med erhvervslivet og den offentlige sektor. Projekterne g\u00e5r p\u00e5 tv\u00e6rs af brancher og omhandler bl.a. kunstig intelligens, Internet of Things, algoritmer og cybersikkerhed. <\/p>\n

L\u00e6s mere p\u00e5 direc.dk<\/a><\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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EXPLAIN-ME<\/strong><\/p>\n

Partnere i projektet EXPLAIN-ME: Learning to Collaborate via Explainable AI in Medical Education
\n<\/b><\/p>\n

    \n
  • DTU<\/li>\n
  • K\u00f8benhavns Universitet<\/li>\n
  • Aalborg Universitet<\/li>\n
  • Roskilde Universitet<\/li>\n
  • CAMES – Copenhagen Academy for Medical Education and Simulation p\u00e5 Rigshospitalet i K\u00f8benhavn<\/li>\n
  • NordSim – Center for f\u00e6rdighedstr\u00e6ning og simulation p\u00e5 Aalborg Universitetshospital<\/li>\n
  • Urologisk Afdeling p\u00e5 Sj\u00e6llands Universitetshospital i Roskilde.<\/li>\n<\/ul>\n

    Projektperiode: 1. oktober 2021 til 30. april 2025<\/p>\n

    Kontakt:<\/strong>
    Aasa Feragen
    Professor p\u00e5 DTU Compute
    M: +45 26 22 04 98
    afhar@dtu.dk<\/p>\n

    Anders Nymark Christensen
    DTU Compute
    +45 45 25 52 58
    anym@dtu.dk<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

    26. november 2021<\/b>
    \nI et nyt DIREC-projekt samarbejder AI-forskere med sygehuse om at skabe mere nyttig AI og AI-algoritmer, der er lettere at forst\u00e5.<\/p>\n","protected":false},"author":3,"featured_media":32796,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"elementor_header_footer","format":"standard","meta":{"footnotes":""},"categories":[176,185,31],"tags":[89,104,63],"class_list":["post-10692","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-da","category-health-tech-da","category-nyheder","tag-impact-story-da","tag-p16-da","tag-ws2"],"yoast_head":"\nForklarlig kunstig intelligens skal \u00f8ge sygehuses brug af AI - DIREC<\/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:\/\/direc.dk\/da\/forklarlig-kunstig-intelligens-skal-oege-sygehuses-brug-af-ai\/\" \/>\n<meta property=\"og:locale\" content=\"da_DK\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Forklarlig kunstig intelligens skal \u00f8ge sygehuses brug af AI - 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