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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":"
Este art\u00edculo define y analiza el fen\u00f3meno del \u00abAI washing\u00bb en el mercado del software de ortodoncia, en el que los proveedores se apropian del lenguaje de la inteligencia artificial para describir la automatizaci\u00f3n convencional del software. Identifica tres formas habituales de \u00abAI washing\u00bb en las plataformas de dise\u00f1o de alineadores transparentes: el \u00abintercambio de segmentaci\u00f3n\u00bb, en el que la segmentaci\u00f3n autom\u00e1tica \u2014una tarea resuelta de visi\u00f3n artificial\u2014 se comercializa como \u00abplanificaci\u00f3n del tratamiento con IA\u00bb, a pesar de que representa el \u00fanico paso automatizado en un flujo de trabajo que, por lo dem\u00e1s, es manual; el \u00abcambio de marca basado en reglas\u00bb, en el que los sistemas expertos que siguen una l\u00f3gica humana codificada se presentan como aprendizaje autom\u00e1tico; y la \u00abimplicaci\u00f3n de velocidad\u00bb, en la que el r\u00e1pido rendimiento del flujo de trabajo, logrado mediante la ingenier\u00eda de factores humanos, se atribuye a la inteligencia artificial. El art\u00edculo distingue estas pr\u00e1cticas de la inteligencia artificial genuina, que requiere aprendizaje autom\u00e1tico, mejora basada en datos y comprensi\u00f3n aut\u00f3noma de la biomec\u00e1nica ortod\u00f3ntica y los sistemas de fuerzas. Al aclarar estas distinciones, el art\u00edculo dota a los cl\u00ednicos y a los equipos de compras de las herramientas necesarias para evaluar cr\u00edticamente las afirmaciones sobre la IA y evitar costosas inversiones err\u00f3neas en software que no ofrece una automatizaci\u00f3n transformadora.<\/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\/es\/wp-json\/wp\/v2\/posts\/1675","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.bestsmiletech.com\/es\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.bestsmiletech.com\/es\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.bestsmiletech.com\/es\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.bestsmiletech.com\/es\/wp-json\/wp\/v2\/comments?post=1675"}],"version-history":[{"count":2,"href":"https:\/\/www.bestsmiletech.com\/es\/wp-json\/wp\/v2\/posts\/1675\/revisions"}],"predecessor-version":[{"id":2087,"href":"https:\/\/www.bestsmiletech.com\/es\/wp-json\/wp\/v2\/posts\/1675\/revisions\/2087"}],"wp:attachment":[{"href":"https:\/\/www.bestsmiletech.com\/es\/wp-json\/wp\/v2\/media?parent=1675"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.bestsmiletech.com\/es\/wp-json\/wp\/v2\/categories?post=1675"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.bestsmiletech.com\/es\/wp-json\/wp\/v2\/tags?post=1675"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}