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KI-Unterstützung",[210,214],{"path":211,"langcode":212},"\u002Fen\u002Fcase-studies\u002Fsmarter-medical-device-classification-powered-by-ai",{"id":213},"en",{"path":207,"langcode":215},{"id":216},"de",{"id":218,"sharedMediaLegend":219,"mediaImage":220,"name":245},"173254",null,{"url":221,"width":222,"height":223,"alt":224,"title":219,"size":225,"mime":226,"variations":219,"headerMinimalVariations":227},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002FMedical%20team%20analyzing%20MRI%20scans%20on%20digital%20tablet_1.jpeg",1920,1280,"Close up of doctors hands pointing at brain xrays images on digital tablet. Medical team in clinic analyzing MRI scans on digital tablet.",90930,"image\u002Fjpeg",[228,233,234,239,244],{"name":229,"url":230,"width":231,"height":232},"FOCAL_POINT32_SMALL","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_3_2_small\u002Fpublic\u002Fimages\u002FMedical%20team%20analyzing%20MRI%20scans%20on%20digital%20tablet_1.jpeg.webp?itok=4FUjabIu",800,533,{"name":229,"url":230,"width":231,"height":232},{"name":235,"url":236,"width":237,"height":238},"FOCAL_POINT169_MEDIUM","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_16_9_medium\u002Fpublic\u002Fimages\u002FMedical%20team%20analyzing%20MRI%20scans%20on%20digital%20tablet_1.jpeg.webp?itok=iehzFQV_",1200,675,{"name":240,"url":241,"width":242,"height":243},"FOCAL_POINT169_LARGE","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_16_9_large\u002Fpublic\u002Fimages\u002FMedical%20team%20analyzing%20MRI%20scans%20on%20digital%20tablet_1.jpeg.webp?itok=youD7E4n",1600,900,{"name":240,"url":241,"width":242,"height":243},"Medical team analyzing MRI scans on digital tablet.jpeg","KI-Plattform für Medizinprodukteklassifizierung ",{"processed":248},"\u003Cp>Zühlke entwickelte mit einer südostasiatischen Gesundheitsbehörde eine KI-Plattform zur Klassifizierung von Medizinprodukten mit 92 % Genauigkeit im Test.\u003C\u002Fp>\n",{"id":218,"sharedMediaLegend":219,"mediaImage":250,"name":245},{"url":221,"width":222,"height":223,"alt":224,"title":219,"size":225,"mime":226,"variations":219},{"id":252,"sharedMediaLegend":219,"mediaImage":253,"name":259},"171983",{"url":254,"width":255,"height":255,"alt":256,"title":219,"size":257,"mime":258,"variations":219},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002Fzuehlke_schriftzug_purple_rgb.png",296,"zuhlke logo",3914,"image\u002Fpng","zuehlke_schriftzug_purple_rgb.png",[261,269,274,301,332,342],{"__typename":262,"id":263,"ctaLink":219,"pgSharedText":264,"pgSharedTitle":268,"pgSharedCtaLink":219},"ParagraphPgText","35143",{"value":265,"processed":266,"format":267},"\u003Cul>\u003Cli data-list-item-id=\"e829ab9958cd084ce102fe16015b05789\">Eine nationale Regulierungsbehörde wollte einen manuellen und zeitintensiven Prozess zur Risikoklassifizierung von Medizinprodukten modernisieren. \u003C\u002Fli>\u003Cli data-list-item-id=\"e6421ec5c213a6fe043dbbbf012b13fb4\">Zühlke entwickelte einen Machine-Learning-gestützten Prototyp, der Produktrisiken mit einer Genauigkeit von 92 Prozent klassifiziert und damit das ursprüngliche Ziel von 80 Prozent deutlich übertrifft. \u003C\u002Fli>\u003Cli data-list-item-id=\"e5db52b2134aba856887b7f4c41248e0c\">Der Prototyp bestätigte, dass sich KI auch in hochregulierten Gesundheitsumgebungen sinnvoll einsetzen lässt und schuf die Grundlage für weitere Automatisierungsschritte.\u003C\u002Fli>\u003C\u002Ful>","\u003Cul>\u003Cli>Eine nationale Regulierungsbehörde wollte einen manuellen und zeitintensiven Prozess zur Risikoklassifizierung von Medizinprodukten modernisieren. \u003C\u002Fli>\u003Cli>Zühlke entwickelte einen Machine-Learning-gestützten Prototyp, der Produktrisiken mit einer Genauigkeit von 92 Prozent klassifiziert und damit das ursprüngliche Ziel von 80 Prozent deutlich übertrifft. \u003C\u002Fli>\u003Cli>Der Prototyp bestätigte, dass sich KI auch in hochregulierten Gesundheitsumgebungen sinnvoll einsetzen lässt und schuf die Grundlage für weitere Automatisierungsschritte.\u003C\u002Fli>\u003C\u002Ful>","full_html","Projektüberblick",{"__typename":262,"id":270,"ctaLink":219,"pgSharedText":271,"pgSharedTitle":273,"pgSharedCtaLink":219},"35144",{"value":272,"processed":272,"format":267},"\u003Cp>Jedes Medizinprodukt – vom Rollstuhl bis zum Herzschrittmacher – muss vor der Zulassung bewertet und einer Risikoklasse zugeordnet werden. Eine präzise Klassifizierung ist entscheidend für den Schutz der öffentlichen Gesundheit.\u003C\u002Fp>\u003Cp>Der bestehende Klassifizierungsprozess der Behörde basierte vollständig auf manueller Prüfung. Dieser Ansatz war zwar sorgfältig, aber zeitaufwendig, schwer skalierbar bei steigenden Einreichungszahlen und anfällig für Inkonsistenzen sowie menschliche Fehler. Die zentrale Frage lautete deshalb: Kann KI Prüfteams dabei unterstützen, schneller und konsistenter zu arbeiten, ohne regulatorische Standards zu gefährden?\u003C\u002Fp>","Die Herausforderung: Klassifizierung als Schutz der öffentlichen Gesundheit",{"__typename":275,"id":276,"caption":219,"imageRight":277,"pgSharedTitle":278,"pgSharedCtaLink":219,"textMedia":279,"pgSharedText":299},"ParagraphPgTextMedia","35145",false,"Der Ansatz: eine strenge KI für eine regulierte Umgebung",{"id":280,"sharedMediaLegend":219,"mediaImage":281,"name":298},"173247",{"url":282,"width":283,"height":284,"alt":285,"title":219,"size":286,"mime":258,"variations":219,"textMediaVariations":287},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002Firrational_AI.png",1048,1173,"Abstract 3D digital structure with glowing blue lines and purple geometric shapes, representing artificial intelligence, data networks and emerging technology.",2055924,[288,292,293,294,297],{"name":289,"url":290,"width":243,"height":291},"FIXED_WIDTH_SMALL","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffixed_width_small\u002Fpublic\u002Fimages\u002Firrational_AI.png.webp?itok=BWAP7JRh",1007,{"name":289,"url":290,"width":243,"height":291},{"name":289,"url":290,"width":243,"height":291},{"name":295,"url":296,"width":283,"height":284},"FIXED_WIDTH_MEDIUM","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffixed_width_medium\u002Fpublic\u002Fimages\u002Firrational_AI.png.webp?itok=pHvvcV0C",{"name":295,"url":296,"width":283,"height":284},"irrational_AI.png",{"value":300,"processed":300,"format":267},"\u003Cp>Der Kunde beauftragte Zühlke \u003Ca href=\"https:\u002F\u002Faws.amazon.com\u002Fmarketplace\u002Fseller-profile?id=seller-k2faq4hwtp3pg\" target=\"_blank\" title=\"Entdecken Sie unseren Marketplace\">über den AWS Marketplace\u003C\u002Fa>. Dadurch konnte das Projekt schnell starten und gleichzeitig die Beschaffungsanforderungen des öffentlichen Sektors erfüllen. Gemeinsam entwickelte das Team eine Proof-of-Concept-Lösung auf Basis öffentlich verfügbarer regulatorischer Daten sowie historischer Einreichungen des Kunden.\u003C\u002Fp>\u003Cp>Die Plattform nutzt Machine Learning (ML), Deep Learning (DL) und Large Language Models (LLMs), um das Risiko von Medizinprodukten anhand der von Herstellern und Vertreibern bereitgestellten Informationen zu bewerten und zu klassifizieren.\u003C\u002Fp>\u003Cp>Nachvollziehbarkeit war von Anfang an Teil der Lösung. Das Modell zeigt, welche Eingabebegriffe die jeweilige Klassifizierungsentscheidung besonders stark beeinflusst haben. Dadurch lässt sich jedes Ergebnis prüfen, erklären und validieren.\u003C\u002Fp>",{"__typename":302,"id":303,"eyebrow":219,"pgQuoteRefQuote":304,"hideImage":277,"hideFunctionAndCompany":277,"bigQuote":277},"ParagraphPgQuote","35147",[305],{"status":7,"id":306,"promote":277,"quotePersonDesc":307,"quotePersonName":308,"quoteText":309,"profileImage":310,"title":331},"27072","Principal Account Manager, Zühlke","Patrick Wong","Die Klassifizierung von Medizinprodukten ist aus gutem Grund stark reguliert. Des-halb wurden diese Entscheidungen schon immer mit großer Sorgfalt und menschli-chem Urteilsvermögen getroffen. Unser Ziel war nicht, diese Standards zu ersetzen, sondern sie zu unterstützen. KI in einem solchen Umfeld einzusetzen, erfordert stren-ge Tests, klare Governance und tiefes Fachwissen.",{"id":311,"sharedMediaLegend":219,"mediaImage":312,"name":330},"173252",{"url":313,"width":314,"height":314,"alt":315,"title":219,"size":316,"mime":226,"variations":219,"quoteVariations":317},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002Fpawo.jpg",2770,"Patrick Wong profile picture",577416,[318,322,325,328,329],{"name":319,"url":320,"width":321,"height":321},"FOCAL_POINT11_EXTRA_SMALL","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_1_1_extra_small\u002Fpublic\u002Fimages\u002Fpawo.jpg.webp?itok=_Ou7Nlua",400,{"name":323,"url":324,"width":231,"height":231},"FOCAL_POINT11_SMALL","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_1_1_small\u002Fpublic\u002Fimages\u002Fpawo.jpg.webp?itok=Z_HVSrtc",{"name":326,"url":327,"width":237,"height":237},"FOCAL_POINT11_MEDIUM","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_1_1_medium\u002Fpublic\u002Fimages\u002Fpawo.jpg.webp?itok=v7znWrTK",{"name":326,"url":327,"width":237,"height":237},{"name":326,"url":327,"width":237,"height":237},"pawo.jpg","Patrick Wong - HSA case study",{"__typename":262,"id":333,"ctaLink":334,"pgSharedText":337,"pgSharedTitle":340,"pgSharedCtaLink":341},"35146",{"title":335,"url":336,"internal":7},"Entdecken Sie unsere KI-Services","\u002Fde\u002Fexpertise\u002Fki-loesungen",{"value":338,"processed":339,"format":267},"\u003Cp>Der Prototyp erzielte unter bewusst strengen Testbedingungen folgende Ergebnisse:\u003C\u002Fp>\u003Cul>\u003Cli data-list-item-id=\"ed8f9fb3cb250f3980587342a4ea3fe4a\">\u003Cstrong>92 Prozent Klassifizierungsgenauigkeit\u003C\u002Fstrong> und damit deutlich über dem Zielwert von 80 Prozent \u003C\u002Fli>\u003Cli data-list-item-id=\"efdf38221686bed08c13b30d6e7619468\">\u003Cstrong>vollständige Nachvollziehbarkeit \u003C\u002Fstrong>aller Klassifizierungsentscheidungen\u003C\u002Fli>\u003Cli data-list-item-id=\"e2520d6ae77e14ce6df048eb1e9a714e5\">\u003Cstrong>Secure-by-Design-Infrastruktur\u003C\u002Fstrong> im Einklang mit den Cloud-Compliance-Anforderungen der öffentlichen Hand \u003C\u002Fli>\u003Cli data-list-item-id=\"ef202d79c5d0ffc4e1ed7c80f302e9164\">\u003Cstrong>validierte Architektur\u003C\u002Fstrong>, geprüft durch den internen Governance-Ausschuss des Kunden sowie einen AWS Solutions Architect\u003C\u002Fli>\u003C\u002Ful>\u003Cp>Der Prototyp lieferte nicht nur einen technischen Machbarkeitsnachweis. Er schuf auch die Vertrauens- und Governance-Grundlagen, die die Behörde benötigt, um KI-gestützte Entscheidungen künftig auf weitere regulatorische Prozesse auszuweiten – mit demselben Anspruch an Transparenz, Sicherheit und Sorgfalt.\u003C\u002Fp>\u003Cp>\u003Cstrong>Erfahren Sie, wie Zühlke Sie dabei unterstützt, neue KI-Möglichkeiten in praktische und vertrauenswürdige Lösungen zu überführen.\u003C\u002Fstrong>\u003C\u002Fp>","\u003Cp>Der Prototyp erzielte unter bewusst strengen Testbedingungen folgende Ergebnisse:\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>92 Prozent Klassifizierungsgenauigkeit\u003C\u002Fstrong> und damit deutlich über dem Zielwert von 80 Prozent \u003C\u002Fli>\u003Cli>\u003Cstrong>vollständige Nachvollziehbarkeit \u003C\u002Fstrong>aller Klassifizierungsentscheidungen\u003C\u002Fli>\u003Cli>\u003Cstrong>Secure-by-Design-Infrastruktur\u003C\u002Fstrong> im Einklang mit den Cloud-Compliance-Anforderungen der öffentlichen Hand \u003C\u002Fli>\u003Cli>\u003Cstrong>validierte Architektur\u003C\u002Fstrong>, geprüft durch den internen Governance-Ausschuss des Kunden sowie einen AWS Solutions Architect\u003C\u002Fli>\u003C\u002Ful>\u003Cp>Der Prototyp lieferte nicht nur einen technischen Machbarkeitsnachweis. 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