{"id":20573,"date":"2026-07-17T09:49:26","date_gmt":"2026-07-17T02:49:26","guid":{"rendered":"https:\/\/fkg.ugm.ac.id\/?p=20573"},"modified":"2026-07-17T09:49:28","modified_gmt":"2026-07-17T02:49:28","slug":"kecerdasan-buatan-masuk-ruang-praktik-ortodonti-seberapa-jauh-bisa-diandalkan","status":"publish","type":"post","link":"https:\/\/fkg.ugm.ac.id\/en\/kecerdasan-buatan-masuk-ruang-praktik-ortodonti-seberapa-jauh-bisa-diandalkan\/","title":{"rendered":"Artificial Intelligence Enters the Orthodontic Clinic: How Reliable Can It Be?"},"content":{"rendered":"<p class=\"wp-block-paragraph\"><strong>Imagine a dentist having to identify 19 anatomical landmarks on a lateral cephalometric radiograph\u2014one by one, with meticulous precision, while also considering the patient\u2019s unique clinical condition. The process can take more than ten minutes. An artificial intelligence algorithm can perform the same task in three seconds, achieving a detection rate of 97.30% within a two-millimeter deviation.<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph translation-block\">That figure is not science fiction. It comes from a scoping review published in the Journal of Dentistry in early 2025\u2014a systematic review that mapped 71 studies on the application of artificial intelligence (AI) in orthodontic diagnosis and treatment planning. One of the authors was drg. Rellyca Sola Gracea, Sp. RKG. Subsp. RDP (K), an Indonesian researcher affiliated with the OMFS-IMPATH research group at KU Leuven, Belgium.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>From Three Seconds to Thousands of Images: What Can AI Do?<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The research team, led by Reinhilde Jacobs, searched PubMed, Web of Science, and Embase through June 2023. Of the 943 articles identified, 71 passed the rigorous selection process and were categorized into three major domains: diagnosis (29 studies), anatomical landmark detection (20 studies), and treatment planning (22 studies).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The results were striking. In the diagnostic domain, AI was shown to classify clinical and radiological images with accuracy of up to 99% in just 0.08 minutes. Assessment of cervical vertebral maturation stages\u2014which normally requires experienced clinical judgment\u2014could be completed by AI in 0.1 seconds, with a maximum reported accuracy of 90%. For diagnosing malocclusion from intraoral images, one CNN model achieved 99% accuracy in detecting conditions such as crowding, spacing, overjet, crossbite, open bite, and deep bite.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Anatomical landmark detection was the most extensively studied topic in the review. Most studies trained AI networks to recognize 19 anatomical landmarks on lateral cephalograms\u2014a task that has long been a cornerstone of orthodontic cephalometric analysis. Interestingly, AI demonstrated better reproducibility than humans, meaning that its measurements were more consistent and less dependent on who performed the analysis.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Treatment Planning: AI Can Help, but It Cannot Work Independently<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The findings in the treatment-planning domain were equally interesting. Two independent studies showed that artificial neural networks could predict the need for tooth extraction for orthodontic purposes with approximately 93\u201394% accuracy. Another system successfully assisted with orthognathic surgical planning, achieving 96.3% diagnostic agreement with expert clinicians.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">One model was even designed to predict patients\u2019 experiences during Invisalign treatment, including pain, anxiety, and quality of life, with predictive performance ranging from 88% to 93%. This goes beyond technical efficiency and touches on the human experience of medical treatment.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">However, there is a clear limitation. The review emphasized that AI is not yet capable of independently developing treatment plans. Human oversight remains irreplaceable.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong><em>\u201cAI should be regarded as a decision-support tool that enhances, rather than replaces, the critical role of clinical judgment in orthodontic care.\u201d\n\u2014 Gracea et al., Journal of Dentistry, 2025<\/em><\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Great Promise, with Serious Caveats<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Behind these impressive figures are several important considerations that need to be examined carefully.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">First, there is the issue of data. Nearly all AI models in the studies were trained using limited and relatively non-diverse datasets. When tested on different public datasets, model performance often declined significantly. This is a problem of generalizability\u2014a model that performs well at one hospital may not perform equally well at another.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Second, there is the issue of the gold standard. Many studies use expert clinicians\u2019 assessments as the reference standard. Yet experts may also disagree when evaluating the same case. When AI is trained on data that are themselves inconsistent, the resulting models may inherit that uncertainty.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Third, there is the \u201cblack box\u201d problem. Complex deep-learning algorithms often cannot explain why they make a particular decision. This is not merely a technical issue\u2014it concerns clinical trust and legal accountability, both of which remain insufficiently defined.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">More than 80% of the 71 articles analyzed were published between 2021 and 2023. This surge in publications reflects how rapidly the field is developing. However, the speed of research does not automatically translate into clinical readiness.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Human\u2013Machine Collaboration: A Future Under Construction<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The review does not end on a pessimistic note. On the contrary, it maps out areas that remain largely unexplored. Predicting the eruption patterns of impacted teeth, simulating long-term facial growth, automating study-model analysis, and assessing orthodontic indices using AI are all identified as promising areas that still require further research.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Most importantly, the review emphasizes that AI will not replace orthodontists. Instead, it may be most useful as a bridge for clinicians who are still developing their experience or as a second-check tool that can help identify findings that might be overlooked by a fatigued human eye.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Artificial intelligence is entering the orthodontic clinic not to take over the dentist\u2019s chair. It is coming to sit beside the dentist\u2014to assist, accelerate, and provide reminders. How reliable this collaboration ultimately becomes will depend greatly on how seriously the clinical and scientific communities engage in shaping it.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Source DOI: <a href=\"https:\/\/doi.org\/10.1016\/j.jdent.2024.105442\">https:\/\/doi.org\/10.1016\/j.jdent.2024.105442<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Authors: Anny Anggraini; drg. Achmad Zam Zam Aghasy, M.Kes.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Photo: Freepik<\/p>","protected":false},"excerpt":{"rendered":"<p>Bayangkan seorang dokter gigi yang harus menandai 19 titik anatomis pada foto rontgen sefalometri lateral \u2014 satu per satu, dengan ketelitian tinggi, sambil mempertimbangkan kondisi klinis pasien yang unik. Proses itu bisa memakan waktu belasan menit. Sebuah algoritma kecerdasan buatan melakukannya dalam tiga detik, dengan tingkat keberhasilan deteksi mencapai 97,30% dalam batas deviasi dua milimeter. [&hellip;]<\/p>\n","protected":false},"author":615,"featured_media":20574,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[359,136],"tags":[298,307,289,291,297],"class_list":["post-20573","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artikel","category-berita-terbaru","tag-sdg-10-mengurangi-ketidaksetaraan","tag-sdg-17-kemitraan-untuk-mencapai-tujuan","tag-sdg-3-kesehatan-dan-kesejahteraan-yang-baik","tag-sdg-4-pendidikan-berkualitas","tag-sdg-9-industri-inovasi-dan-infrastruktur"],"gutentor_comment":0,"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Kecerdasan Buatan Masuk Ruang Praktik Ortodonti: Seberapa Jauh Bisa Diandalkan? - Fakultas Kedokteran Gigi<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fkg.ugm.ac.id\/en\/kecerdasan-buatan-masuk-ruang-praktik-ortodonti-seberapa-jauh-bisa-diandalkan\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Kecerdasan Buatan Masuk Ruang Praktik Ortodonti: Seberapa Jauh Bisa Diandalkan? - Fakultas Kedokteran Gigi\" \/>\n<meta property=\"og:description\" content=\"Bayangkan seorang dokter gigi yang harus menandai 19 titik anatomis pada foto rontgen sefalometri lateral \u2014 satu per satu, dengan ketelitian tinggi, sambil mempertimbangkan kondisi klinis pasien yang unik. Proses itu bisa memakan waktu belasan menit. Sebuah algoritma kecerdasan buatan melakukannya dalam tiga detik, dengan tingkat keberhasilan deteksi mencapai 97,30% dalam batas deviasi dua milimeter. 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