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The ways of using machine learning in dentistry.

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Summary

Artificial intelligence (AI) and machine learning offer promising advancements for digital dentistry, improving diagnosis and treatment planning across specialties. However, widespread clinical adoption of AI-driven clinical decision support systems (CDSS) requires further development in technology and business integration.

Keywords:
CDSSartificial intelligenceclinical decision support systemsdentistrymachine learning

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Area of Science:

  • Dentistry
  • Computer Science
  • Artificial Intelligence

Background:

  • Digitalization is transforming dentistry, increasing demands for advanced diagnostic and treatment planning tools.
  • Accurate diagnosis is crucial in specialties like orthodontics, maxillofacial surgery, periodontics, and prosthetics for effective patient care.
  • Current diagnostic processes rely on specialist knowledge but are prone to multi-factorial errors.

Purpose of the Study:

  • To review the latest applications of artificial intelligence (AI), including clinical decision support systems (CDSS) and genetic algorithms (GAs), in dental research and practice.
  • To analyze the real-world application and credibility of emerging AI technologies in various dental specialties.
  • To highlight the potential of AI in improving diagnostic accuracy and treatment planning in dentistry.

Main Methods:

  • Review of recent literature on AI applications in dentistry.
  • Analysis of studies employing machine learning, AI, and genetic algorithms (GAs) in dental diagnostics and treatment planning.
  • Examination of the development and implementation of clinical decision support systems (CDSS) in dental settings.

Main Results:

  • AI and multiparametric pattern recognition methods show significant potential for enhancing diagnostic accuracy and treatment planning in dentistry.
  • Various AI applications, including CDSS and GAs, are being explored across major dental specialties.
  • Despite advancements, the widespread clinical implementation of CDSS in dental practice faces methodical, technological, and business challenges.

Conclusions:

  • AI presents a significant opportunity to improve patient health outcomes in dentistry by reducing diagnostic errors and optimizing treatment plans.
  • Continued research and development are necessary to overcome barriers to the widespread adoption of AI-powered clinical decision support systems in dental clinics.
  • The integration of AI technologies holds promise for the future of digital dentistry, enhancing both research and clinical practice.