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<\/p>","protected":false},"excerpt":{"rendered":"Against the backdrop of genuine AI\u2019s transformative potential, the orthodontic software market has developed a troubling habit of co-opting the language of artificial intelligence to describe conventional software automation. This practice, known as \u201cAI washing,\u201d takes several forms.<\/p>\n
The most common variant is the segmentation swap: a platform markets its auto-segmentation capability as \u201cAI treatment planning\u201d when segmentation is the only AI-powered step in an otherwise manual workflow. Six of the ten platforms offer some form of tooth segmentation automation, but only one extends AI to autonomous plan generation. Segmentation is a well-solved computer vision problem. Treatment planning requires understanding orthodontic biomechanics, force systems, and clinical outcomes\u2014a challenge most vendors have not attempted.<\/p>\n
Another variant is the rule-based rebrand. OnyxCeph explicitly describes its technology as using \u201crule-based control modules\u201d and \u201cCA treatment philosophy for automatic transposition.\u201d This is expert-system logic, not machine learning. The system follows encoded rules written by human experts; it does not learn from data or improve with experience. There is nothing wrong with rule-based systems\u2014they can be reliable and transparent\u2014but presenting them as AI is misleading.<\/p>\n
A third variant is the speed implication: vendors equate fast workflows with AI intelligence. OrthoUp achieves its approximately fifteen-minute planning time through excellent workflow engineering. The speed is real and valuable, but it comes from human-factor design, not from machine learning.<\/p>\n
<\/p>","protected":false},"excerpt":{"rendered":"
Denna artikel definierar och unders\u00f6ker fenomenet \u201dAI-washing\u201d p\u00e5 marknaden f\u00f6r tandregleringsprogramvara, d\u00e4r leverant\u00f6rer utnyttjar begrepp fr\u00e5n artificiell intelligens f\u00f6r att beskriva konventionell programvaruautomatisering. Den identifierar tre vanliga former av AI-washing inom designplattformar f\u00f6r genomskinliga tandst\u00e4llningar: segmenteringsbytet, d\u00e4r autosegmentering \u2013 en redan l\u00f6st uppgift inom datorseende \u2013 marknadsf\u00f6rs som \u201dAI-behandlingsplanering\u201d trots att den utg\u00f6r det enda automatiserade steget i ett i \u00f6vrigt manuellt arbetsfl\u00f6de; den regelbaserade omprofileringen, d\u00e4r expertsystem som f\u00f6ljer kodad m\u00e4nsklig logik presenteras som maskininl\u00e4rning; och hastighetsimplikationen, d\u00e4r snabb arbetsfl\u00f6desprestanda som uppn\u00e5s genom m\u00e4nsklig faktorteknik tillskrivs AI-intelligens. Artikeln skiljer dessa metoder fr\u00e5n \u00e4kta artificiell intelligens, som kr\u00e4ver maskininl\u00e4rning, datadriven f\u00f6rb\u00e4ttring och autonom f\u00f6rst\u00e5else av ortodontisk biomekanik och kraftsystem. Genom att klarg\u00f6ra dessa skillnader ger artikeln kliniker och ink\u00f6psteam verktyg f\u00f6r att kritiskt utv\u00e4rdera p\u00e5st\u00e5enden om AI och undvika kostsamma felinvesteringar i programvara som inte levererar transformativ automatisering.<\/p>","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"default","ast-global-header-display":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","footnotes":""},"categories":[770,1],"tags":[667,163,668,167,295,299,403,468,635,643,649,661,662,663,664,665,666],"class_list":["post-1675","post","type-post","status-publish","format-standard","hentry","category-ai-aligner-design-software","category-blog","tag-software-evaluation","tag-best-smile-tech-ai","tag-ai-claims-verification","tag-digital-orthodontics","tag-clear-aligner-design","tag-onyxceph","tag-orthodontic-software","tag-ai-treatment-planning","tag-ai-washing","tag-machine-learning-orthodontics","tag-clear-aligner-ai","tag-fake-ai","tag-rule-based-system","tag-automation-vs-ai","tag-dental-ai-marketing","tag-segmentation-automation","tag-orthoup"],"_links":{"self":[{"href":"https:\/\/www.bestsmiletech.com\/sv\/wp-json\/wp\/v2\/posts\/1675","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.bestsmiletech.com\/sv\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.bestsmiletech.com\/sv\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.bestsmiletech.com\/sv\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.bestsmiletech.com\/sv\/wp-json\/wp\/v2\/comments?post=1675"}],"version-history":[{"count":2,"href":"https:\/\/www.bestsmiletech.com\/sv\/wp-json\/wp\/v2\/posts\/1675\/revisions"}],"predecessor-version":[{"id":2087,"href":"https:\/\/www.bestsmiletech.com\/sv\/wp-json\/wp\/v2\/posts\/1675\/revisions\/2087"}],"wp:attachment":[{"href":"https:\/\/www.bestsmiletech.com\/sv\/wp-json\/wp\/v2\/media?parent=1675"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.bestsmiletech.com\/sv\/wp-json\/wp\/v2\/categories?post=1675"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.bestsmiletech.com\/sv\/wp-json\/wp\/v2\/tags?post=1675"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}