Artificial Intelligence in Pediatric Dentistry

Authors

  • Kuruva Sharvani Post Graduate, Department of Pediatric and Preventive Dentistry, Anil Neerukonda Institute of Dental Sciences, Visakhapatnam, Andhra Pradesh.
  • K Balakrishna Professor,Department of Pediatric and Preventive Dentistry, Anil Neerukonda Institute Visakhapatnam, Andhra Pradesh.
  • Madhavi Krishna Associate Professor,Department of Pediatric and Preventive Dentistry, Anil Neerukonda Institute Visakhapatnam, Andhra Pradesh.
  • KVK Santosh Kumar Assistant Professor, Department of Pediatric and Preventive Dentistry, Anil Neerukonda Institute Visakhapatnam, Andhra Pradesh.
  • Navaneet Babu Senapathi Associate Professor,Department of Pediatric and Preventive Dentistry, Anil Neerukonda Institute Visakhapatnam, Andhra Pradesh.
  • S Pavani Reddy Assistant Professor, Department of Pediatric and Preventive Dentistry, Anil Neerukonda Institute Visakhapatnam, Andhra Pradesh.

DOI:

https://doi.org/10.48165/gm7nvn72

Keywords:

Artificial intelligence, Pediatric Dentistry, Applications

Abstract

 Artificial intelligence (AI) has gained significant momentum in the field of pediatric dentistry, with applications aimed at improving diagnostics, treatment planning, patient engagement, and research. The integration of AI technologies in dental practices offers potential to enhance the accuracy of diagnoses, predict treatment outcomes, customize patient-specific treatments, and streamline practice management. AI-driven tools, such as machine learning algorithms, can analyze complex dental data, including radiographs, photographs, and clinical notes, to aid in early detection and management of oral diseases prevalent in children. Moreover, AI can provide educational resources tailored to young patients, fostering better oral health habits.

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Published

2024-02-20

Issue

Vol. 7 No. 1 (2024): Academia Journal of Medicine(Jan-Jun)

Section

Review Paper

How to Cite

Artificial Intelligence in Pediatric Dentistry . (2024). Academia Journal of Medicine, 7(1), 1–5. https://doi.org/10.48165/gm7nvn72