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Getting this right is a direct safeguard for public health.\u003C\u002Fp>\u003Cp>The authority's classification process relied entirely on manual review. While thorough, it was time-intensive, difficult to scale as submission volumes grew, and vulnerable to inconsistency and human error. The question was clear: could AI help reviewers work faster and more consistently, while maintaining the standards their mandate requires? \u003C\u002Fp>","The challenge: classification that protects public health",{"__typename":271,"id":272,"caption":215,"imageRight":273,"pgSharedTitle":274,"pgSharedCtaLink":215,"textMedia":275,"pgSharedText":295},"ParagraphPgTextMedia","35145",false,"The approach: rigorous AI for a regulated environment",{"id":276,"sharedMediaLegend":215,"mediaImage":277,"name":294},"173247",{"url":278,"width":279,"height":280,"alt":281,"title":215,"size":282,"mime":254,"variations":215,"textMediaVariations":283},"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,[284,288,289,290,293],{"name":285,"url":286,"width":239,"height":287},"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":285,"url":286,"width":239,"height":287},{"name":285,"url":286,"width":239,"height":287},{"name":291,"url":292,"width":279,"height":280},"FIXED_WIDTH_MEDIUM","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffixed_width_medium\u002Fpublic\u002Fimages\u002Firrational_AI.png.webp?itok=pHvvcV0C",{"name":291,"url":292,"width":279,"height":280},"irrational_AI.png",{"value":296,"processed":296,"format":263},"\u003Cp>The client engaged Zühlke \u003Ca href=\"https:\u002F\u002Faws.amazon.com\u002Fmarketplace\u002Fseller-profile?id=seller-k2faq4hwtp3pg\" title=\"Discover our AWS Marketplace solutions\" target=\"_blank\">through the AWS Marketplace\u003C\u002Fa>, enabling a fast start while meeting public sector procurement requirements. Together, the team built a proof-of-concept solution trained on publicly available regulatory data and the client's own historical submissions.\u003C\u002Fp>\u003Cp>The platform leverages machine learning (ML) and deep learning (DL) techniques, including large language models (LLMs), to assess and classify the risk of medical devices based on information provided by the distributor.\u003C\u002Fp>\u003Cp>Traceability was built in from the start. The model highlights which input terms most strongly influenced each classification decision, so every result can be inspected, explained, and validated. \u003C\u002Fp>",{"__typename":298,"id":299,"eyebrow":215,"pgQuoteRefQuote":300,"hideImage":273,"hideFunctionAndCompany":273,"bigQuote":273},"ParagraphPgQuote","35147",[301],{"status":7,"id":302,"promote":273,"quotePersonDesc":303,"quotePersonName":304,"quoteText":305,"profileImage":306,"title":327},"27072","Principal Account Manager, Zühlke","Patrick Wong","Medical device classification is highly regulated for good reason, which is why these decisions have always been handled with great care and human judgment. Our goal wasn't to replace that rigour, but to support it. 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Always with the same commitment to rigour, transparency, and public safety.\u003C\u002Fp>\u003Cp>\u003Cstrong>Explore how Zühlke can help you translate emerging AI capabilities into practical, trusted solutions. \u003C\u002Fstrong>\u003C\u002Fp>","\u003Cp>The prototype achieved the following outcomes during conservative rigorous testing:\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>92% classification accuracy\u003C\u002Fstrong>, exceeding the 80% benchmark target.\u003C\u002Fli>\u003Cli>\u003Cstrong>Full traceability\u003C\u002Fstrong> of every classification decision.\u003C\u002Fli>\u003Cli>\u003Cstrong>Secure-by-design infrastructure \u003C\u002Fstrong>aligned with government cloud compliance requirements.\u003C\u002Fli>\u003Cli>\u003Cstrong>Validated architecture\u003C\u002Fstrong>, reviewed by the client's internal oversight committee and an AWS Solutions Architect.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>The prototype didn't just deliver a technical proof point – it built the confidence and governance foundations the authority needs to expand AI-assisted decision-making across other regulatory workflows. Always with the same commitment to rigour, transparency, and public safety.\u003C\u002Fp>\u003Cp>\u003Cstrong>Explore how Zühlke can help you translate emerging AI capabilities into practical, trusted solutions. \u003C\u002Fstrong>\u003C\u002Fp>","The results: a proven foundation for responsible AI adoption",{"title":331,"url":332,"internal":7},{"__typename":298,"id":339,"eyebrow":215,"pgQuoteRefQuote":340,"hideImage":273,"hideFunctionAndCompany":273,"bigQuote":273},"35148",[341],{"status":7,"id":342,"promote":273,"quotePersonDesc":343,"quotePersonName":344,"quoteText":345,"profileImage":346,"title":363},"27073","Expert Data Scientist, Zühlke","Ricardo Martins","When a device is described as being 'intended for surgery', the term 'surgery' carries greater weight in determining its risk class. 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