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Related Experiment Video

Updated: Apr 23, 2026

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
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Dental Provider Experiences with AI Radiograph Annotation: A Qualitative Case Study.

K L Schroeder1, L Slashcheva2, L J Heaton1

  • 1Analytics and Data Insights, CareQuest Institute for Oral Health, Boston, MA, USA.

JDR Clinical and Translational Research
|April 22, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) radiograph annotation software assists dental professionals but does not replace critical thinking for patient-centered care. Successful integration requires structured onboarding, training, and support for optimal use in oral health.

Keywords:
artificial intelligencedecision-makingdental hygienistsdental therapistsdentistspatient-centered care

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Area of Science:

  • Oral health informatics
  • Dental diagnostics
  • Artificial intelligence in healthcare

Background:

  • Artificial intelligence (AI) tools are increasingly used in dentistry for diagnosis, treatment planning, and education.
  • Radiographic annotation software, a type of AI, highlights potential oral disease indicators on dental radiographs.

Purpose of the Study:

  • To explore the challenges and facilitators influencing the adoption and use of AI radiograph annotation software among oral health professionals.
  • To understand the experiences of dental providers with AI annotation software within a large US healthcare organization.

Main Methods:

  • A qualitative collective case study design was employed.
  • Semi-structured interviews were conducted online with dentists, dental hygienists, and dental therapists across five dental clinics.
  • Abductive coding was used to identify themes related to providers' experiences with the AI software.

Main Results:

  • Key themes included initial impressions, evolving beliefs, utilization patterns, diagnostic confidence, treatment plan alignment, and patient-centered care considerations.
  • Providers recognized the AI software as a decision-support tool, not a replacement for clinical judgment.
  • Unexpected benefits and challenges associated with the AI annotation software were identified.

Conclusions:

  • Findings offer insights for healthcare organizations on supporting the implementation of AI software protocols for oral health professionals.
  • Effective AI integration necessitates structured onboarding, continuous training, and peer support to optimize its value.
  • Adoption of AI tools can enhance diagnostic confidence, patient engagement, and early disease detection, potentially improving oral health outcomes.