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113 - Perceptions of AI Based Software for Caries Detection Among Active Members of AAPD

Friday, May 23, 2025
1:40 PM - 2:00 PM MDT
Has Audio

    Presenting Author(s)

  • Ria Vij, DMD

    Pediatric Dental Resident
    Montefiore Medical Center
    Montefiore Medical Center
    New York, New York, United States

    • Research Mentor(s)

  • Annie Herman Perel, DMD

    PGY-2
    Montefiore Medical Center
    Montefiore Medical Center
    Bronx, New York, United States

    • Program Director(s)

  • AL

    Alice Lee, DDS

    Associate Director/Assistant Professor
    Montefiore Medical Center
    Bronx, New York, United States




Perceptions of AI-Based Software for Caries Detection Among Pediatric Dental Providers

Perceptions of AI-Based Software for Caries Detection Among Pediatric Dental Providers



Authors:


Ria Vij, DMD and Annie Herman Perel, DMD



Purpose:



The purpose of this study is to assess among pediatric dentists their current knowledge and usage of AI-based software
used for caries detection, their current and future perceptions of AI-based software used for caries detection, and
determine differences in their perceptions based on several different demographics.




Methods:



This is a cross-sectional study in which data collection occurred over a period of 2 weeks in the month of March 2025.
Following IRB approval, a survey was made and sent out via SurveyMonkey to active and post-doctoral members of the
American Academy of Pediatric Dentistry (AAPD). Study personnel then collected data for statistical analysis.




Results:



Results revealed that the majority of respondents (92.02%) do not currently use AI software for caries detection, and
90.49% have not received formal training or continuing education on this topic. Of the 21 respondents using AI-based
software, most utilize it as an adjunct diagnostic tool (95.24%) and for enhancing patient education (61.90%), rather
than as a primary diagnostic method. Pediatric dentists identified key benefits of AI-based caries detection, including
improved diagnostic accuracy (76.42%), enhanced patient education (61.79%), and building patient rapport/trust (47.15%).
However, significant concerns remain, with most respondents citing potential inaccuracies in diagnosis (78.88%), cost
of AI software (76.49%), and lack of provider education/training (54.98%). There was a borderline association noted
between time out of residency and overall familiarity with AI. Those with greater than 10 years of experience reported
to be less familiar with AI used for caries detection (p=0.052, where statistical significance is claimed at p≤.05).




Conclusion:



Artificial intelligence (AI) is a relatively new and groundbreaking innovation in the field of dentistry, with the
potential to revolutionize diagnostic processes such as caries detection. This study reveals a significant gap between
the perceived benefits of AI-based caries detection tools and their actual use in pediatric dentistry. While many
practitioners recognize AI's potential to improve diagnostic accuracy, efficiency, and patient trust, adoption remains
low due to limited familiarity and training. Most users see AI as a supplemental tool, and concerns about accuracy and
lack of clinical validation persist. These findings highlight the need for targeted education and further research to
support wider integration of AI in pediatric practice.