Journals / Sağlık Bilimleri Dergisi / 2021 / Cilt: 30 - Sayı: 3

ARTIFICIAL INTELLIGENCE IN ORAL AND MAXILLOFACIAL RADIOLOGY

ORAL VE MAKSİLLOFASİYAL RADYOLOJİ’DE YAPAY ZEKA

Pages
346–351
Publication Date
2021-12-24
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This review revises a general analysis of AI methods, particularly those related to image-based tasks in oral and maxillofacial radiology.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Technology has progressed rapidly since the 90s and these developments took their place in our daily life.
📊
Key Findings & Data:There has been a tremendous improvement and a marked increase in the evolution of artificial intelligence (AI) over the past decade, including dentistry.
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Conclusion & Impact:This review revises a general analysis of AI methods, particularly those related to image-based tasks in oral and maxillofacial radiology.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Derin öğrenme · diş hekimliği · oral radyoloji

Abstract

Technology has progressed rapidly since the 90s and these developments took their place in our daily life. There has been a tremendous improvement and a marked increase in the evolution of artificial intelligence (AI) over the past decade, including dentistry. Regardless of many developments, AI is still in its infancy, but its potential is unlimited. The evolution of artificial intelligence enables the analysis of big data that provides reliable information and improves decision making. The technological adaptation we need to demonstrate and a thorough knowledge of the subject will not only help better and more precise patient care, but also reduce the clinician's workload. AI is important in dentistry, especially in oral and maxillofacial radiology for patient diagnosis, storage of patient data, and providing an improved healthcare service for patients. AI is also slowly but steadily penetrating the field of oral and maxillofacial radiology. This review revises a general analysis of AI methods, particularly those related to image-based tasks in oral and maxillofacial radiology.

Özet

Teknoloji 90’lardan günümüze hızlı ilerleme kaydetti ve bu gelişmeler günlük yaşamımızda yerlerini almıştır. Son on yılda yapay zekanın (Artificial Intelligence, AI) evriminde, diş hekimliğini de kapsayan muazzam bir gelişme izlenmektedir. Pek çok gelişmeden bağımsız olarak, AI hala emekleme aşamasında olmakla birlikte potansiyeli sınırsızdır. Yapay zekanın evrimi, güvenilir bilgi sağlayan ve karar verme sürecini iyileştiren büyük verilerin analizini mümkün kılmaktadır. Göstermemiz gereken teknolojik adaptasyon ve konu ile ilgili kapsamlı bilgi sahibi olmak, sadece daha iyi ve hassas hasta bakımına yardımcı olmakla kalmayacak, aynı zamanda klinisyenin iş yükünü de azaltacaktır. AI, diş hekimliğinde özellikle hasta teşhisi, hasta verilerinin depolanması ve hastalar için gelişmiş bir sağlık hizmeti sağlamak için oral ve maksillofasiyal radyolojide önemli olup,AI, oral ve maksillofasiyal radyoloji alanına da yavaş ama istikrarlı bir şekilde nüfuz etmektedir. Bu derleme, AI yöntemlerinin genel bir analizini, özellikle oral ve maksillofasiyal radyolojide görüntü tabanlı görevlerle ilgili olanları gözden geçirmektedir.

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Cite This Article

ÖZKESİCİ, M. Y., YILMAZ, S. (2021). ARTIFICIAL INTELLIGENCE IN ORAL AND MAXILLOFACIAL RADIOLOGY. Sağlık Bilimleri Dergisi, Cilt: 30 - Sayı: 3, 346–351. https://ajindex.com/articles/397097

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References (12)

  1. McCarthy J, Minsky ML, Rochester N, Shannon CE. A proposal for the dartmouth summer research project on artificial intelligence. AI Magazine 2006; 27:12-12.
  2. Alexander B, John S, Aralamoodu PO. Artificial intelligence in dentistry: current concepts and a peep into the future. International Journal of Advanced Research 2018; 6:1105-1108.
  3. Thrall JH, Li X, Li Q, et al. Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. Jounal of the American College of Radiology 2018; 15:504-508.
  4. Wong SH, Al-Hasani H, Alam Z, Alam A. Artificial intelligence in radiology: how will we be affected? Eur J Radiol 2019; 29:141-143.
  5. Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Ann Surg 2018; 268:70-76. Deshmukh S. Artificial intelligence in dentistry. International Clinical Dental Research Organisation 2018; 10:47-48.
  6. Park WJ, Park J-B. History and application of artificial neural networks in dentistry. European Journal of Dentistry 2018; 12:594-601. Aslan E. Yabancı dil öğretiminde robot öğretmenle. Ondokuz Mayıs Üniversitesi Eğitim Fakültesi Dergisi 2014; 33:15-26. Shortliffe EH. Computer programs to support clinical decision making. JAMA 1987; 258:61-66.
  7. Ishak WHW, Siraj F. Artificial intelligence in medical application: An exploration. Health İnformatics Europe Journal 2002; 16. Best ML, Wade KW. The Internet and democracy: global catalyst or democratic dud? Bulletin of Science, Technology & Society 2009; 29:255-271.
  8. Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology 2017; 2:230-243.
  9. Salari N, Shohaimi S, Najafi F, Nallappan M, Karishnarajah I. A novel hybrid classification model of genetic algorithms, modified k-nearest neighbor and developed backpropagation neural network. PLOS ONE 2014; 9:e112987. Hwang JJ, Azernikov S, Efros AA, Yu SX. Learning beyond human expertise with generative models for dental restorations. 2018.
  10. Khanna DSS, Dhaimade PA. Artificial intelligence: transforming dentistry today. Indian Journal of Basic and Applied Medical Research. 2017; 6:161-167
  11. Goodfellow I, Bengio Y, Courville A. Deep learning. Healthcare Informatic Research 2016; 22:351-354. Bahnsen AC. Building AI applications using deep learning. 2016.
  12. Tang A, Tam R, Cadrin-Chênevert A, et al. Canadian association of radiologists white paper on artificial intelligence in radiology. Can Assoc Radiol J