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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 artigo define e analisa o fen\u00f3meno do \u00abAI washing\u00bb no mercado do software de ortodontia, em que os fornecedores se servem da terminologia da intelig\u00eancia artificial para descrever a automatiza\u00e7\u00e3o convencional do software. Identifica tr\u00eas formas prevalentes de \u00abAI washing\u00bb em plataformas de conce\u00e7\u00e3o de alinhadores transparentes: a troca de segmenta\u00e7\u00e3o, em que a segmenta\u00e7\u00e3o autom\u00e1tica \u2014 uma tarefa j\u00e1 resolvida na vis\u00e3o computacional \u2014 \u00e9 comercializada como \u00abplaneamento de tratamento com IA\u00bb, apesar de representar o \u00fanico passo automatizado num fluxo de trabalho que, de resto, \u00e9 manual; a \u00abrebranding\u00bb baseada em regras, em que sistemas especialistas que seguem l\u00f3gica humana codificada s\u00e3o apresentados como aprendizagem autom\u00e1tica; e a \u00abimplica\u00e7\u00e3o de velocidade\u00bb, em que o desempenho r\u00e1pido do fluxo de trabalho, alcan\u00e7ado atrav\u00e9s da engenharia de fatores humanos, \u00e9 atribu\u00eddo \u00e0 intelig\u00eancia da IA. O artigo distingue estas pr\u00e1ticas da intelig\u00eancia artificial genu\u00edna, que requer aprendizagem autom\u00e1tica, melhoria orientada por dados e compreens\u00e3o aut\u00f3noma da biomec\u00e2nica ortod\u00f4ntica e dos sistemas de for\u00e7a. Ao esclarecer estas distin\u00e7\u00f5es, o artigo capacita os cl\u00ednicos e as equipas de aquisi\u00e7\u00e3o a avaliar criticamente as alega\u00e7\u00f5es relativas \u00e0 IA e a evitar investimentos errados e dispendiosos em software que n\u00e3o proporciona uma automatiza\u00e7\u00e3o 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\/pt\/wp-json\/wp\/v2\/posts\/1675","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.bestsmiletech.com\/pt\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.bestsmiletech.com\/pt\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.bestsmiletech.com\/pt\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.bestsmiletech.com\/pt\/wp-json\/wp\/v2\/comments?post=1675"}],"version-history":[{"count":2,"href":"https:\/\/www.bestsmiletech.com\/pt\/wp-json\/wp\/v2\/posts\/1675\/revisions"}],"predecessor-version":[{"id":2087,"href":"https:\/\/www.bestsmiletech.com\/pt\/wp-json\/wp\/v2\/posts\/1675\/revisions\/2087"}],"wp:attachment":[{"href":"https:\/\/www.bestsmiletech.com\/pt\/wp-json\/wp\/v2\/media?parent=1675"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.bestsmiletech.com\/pt\/wp-json\/wp\/v2\/categories?post=1675"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.bestsmiletech.com\/pt\/wp-json\/wp\/v2\/tags?post=1675"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}