Related Experiment Video
Updated: Sep 28, 2026
Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images
Published on: February 23, 2024
Perceptions of Ethical and Legal Challenges in Artificial Intelligence-Assisted Implant Dentistry Among Dentists: A
Sindhuja S1, Arundhati Singh2, Keerthana Chandrasekaran3
1Department of Oral and Maxillofacial Surgery, Bharath Institute of Higher Education and Research, Bharath University, Chennai, IND.
Introduction:
Artificial intelligence (AI) is being increasingly incorporated into implant dentistry for diagnostic assessment, treatment planning, and clinical decision support. However, its growing clinical application raises concerns regarding patient privacy, informed consent, transparency, accountability, and professional responsibility. The present study aimed to assess dentists' perceptions of the ethical and legal challenges associated with AI-assisted implant dentistry and evaluate their relationship with AI knowledge and professional characteristics.
Materials And Methods:
Dental professionals participated in this cross-sectional questionnaire-based survey from March to August 2022. A validated survey was employed to evaluate professional and demographic traits, AI expertise, and ethical and legal attitudes. The 10 ethical and legal assertions were graded on a 5-point Likert scale, and the 10-item AI knowledge part was scored from 0 to 10, for a total score of 0-40. Cronbach's α = 0.86 indicated that the questionnaire had acceptable internal consistency. Descriptive statistics, multiple linear regression, one-way analysis of variance (ANOVA), and Pearson's correlation were used to examine the data; p < 0.05 was used to determine statistical significance.
Results:
Of 138 dentists approached, 130 participated, yielding a response rate of 94.2%. The participants' mean age was 42.3 ± 7.1 years, and their mean clinical experience was 15.6 ± 5.8 years. The sample included 71 (54.6%) male dentists and 59 (45.4%) female dentists, of whom 50 (38.5%) reported previous AI exposure. The mean AI knowledge score was 5.8 ± 2.4, and the mean ethical and legal score was 27.6 ± 5.6. AI knowledge showed a significant negative correlation with ethical and legal concern (r = -0.482, p < 0.01). Multiple regression explained 37.5% of the variance in ethical and legal concern (R2 = 0.375), with AI knowledge emerging as the strongest independent predictor (β = -0.48, p < 0.001), followed by consultancy practice (β = -0.26, p < 0.001), and previous AI exposure (β = -0.22, p < 0.001). The predicted ethical and legal concern scores progressively decreased from 31.2 in the low knowledge group to 27.4 in the moderate knowledge group and 23.5 in the high knowledge group.
Conclusion:
Greater AI knowledge and previous exposure were associated with fewer ethical and legal concerns regarding AI-assisted implant dentistry. Structured AI education, appropriate regulatory frameworks, data protection, and clear professional accountability are essential for integrating AI into implant practice.
