ACCEPTANCE OF AI-ASSISTED DIAGNOSTIC RADIOGRAPHY IN DENTAL PRACTICE: A CROSS-SECTIONAL STUDY AMONG CLINICIANS
DOI:
https://doi.org/10.71000/kszd6f67Keywords:
Artificial Intelligence, Attitude of Health Personnel, Clinical Decision-Making, Dental Radiography, Diagnostic Imaging, General Practitioners, Technology Acceptance ModelAbstract
Background: Artificial intelligence (AI) is increasingly utilized in dental diagnostics, particularly in radiographic interpretation. Despite its demonstrated clinical potential, the successful implementation of AI in dental practice depends heavily on clinician acceptance, understanding, and trust.
Objective: To determine the acceptance, perceived benefits, and barriers to using AI-assisted radiographic interpretation tools among general dental practitioners in Lahore, Pakistan.
Methods: A cross-sectional survey was conducted over eight months among 412 general dental practitioners working in dental hospitals in Lahore. A structured, validated questionnaire based on the Technology Acceptance Model was used to assess attitudes toward AI tools. Data on perceived usefulness, ease of use, benefits, and barriers were collected. Descriptive statistics, independent t-tests, ANOVA, Pearson’s correlation, and multiple linear regression were used for data analysis. Ethical approval was obtained from the relevant Institutional Review Board (IRB).
Results: Among respondents, 79.1% agreed AI tools were useful, 68.2% found them easy to use, and 73.4% intended to adopt them in practice. Key perceived benefits included improved diagnostic accuracy (85.7%), faster image interpretation (80.6%), and reduced diagnostic errors (77.2%). Barriers included lack of training (74.8%), data privacy concerns (71.1%), and regulatory uncertainty (68.5%). Regression analysis revealed perceived usefulness (β = 0.46), ease of use (β = 0.38), and prior exposure to AI (β = 0.27) as significant predictors of acceptance (p < 0.001).
Conclusion: The findings highlight a generally positive attitude among dental practitioners toward AI-assisted radiographic tools, alongside the need for structured education and clear regulations to overcome adoption barriers.
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Copyright (c) 2025 Sadaf Akram, Naveed Iqbal, Sohail Ahmed Memon, Seema Shafiq, Muhammad Tameem Akhtar, Fatima Rehman (Author)
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