Awareness and Perception of Artificial Intelligence Applications in Forensic Odontology: A Questionnaire-Based Cross-Sectional Study
Ayushi Goyal *
Department of Oral and Maxillofacial Pathology, Government Dental College and Hospital, Chhatrapati Sambhajinagar, Maharashtra, India.
Jayanti G. Humbe
Department of Oral and Maxillofacial Pathology, Government Dental College and Hospital, Chhatrapati Sambhajinagar, Maharashtra, India.
Mandakini Mandale
Department of Oral and Maxillofacial Pathology, Government Dental College and Hospital, Chhatrapati Sambhajinagar, Maharashtra, India.
Vaishali Nandkhedkar
Department of Oral and Maxillofacial Pathology, Government Dental College and Hospital, Chhatrapati Sambhajinagar, Maharashtra, India.
Savita Wagh
Department of Oral and Maxillofacial Pathology, Government Dental College and Hospital, Chhatrapati Sambhajinagar, Maharashtra, India.
*Author to whom correspondence should be addressed.
Abstract
Background: Artificial intelligence (AI) is increasingly influencing diagnostic and analytical practices in dentistry and forensic sciences. In forensic odontology, AI has been explored as an adjunctive tool for human identification, age estimation, bite mark analysis and dental record comparison. However, its practical adoption depends on professional awareness, perceived usefulness and recognition of ethical and technical limitations.
Aim: To assess awareness, utilisation and perceptions regarding AI applications in forensic odontology among dental professionals.
Materials and Methods: A questionnaire-based cross-sectional study was conducted over 6 months using a simple random sampling method among 209 dental professionals, including undergraduate students, postgraduate students, teaching faculty, non-teaching faculty and private practitioners. A structured and validated questionnaire assessed awareness of AI, current utilisation, perceived advantages, limitations and attitudes towards its integration into forensic odontology. Data were analysed using SPSS software, and descriptive statistics were used to calculate frequencies and percentages.
Results: Awareness of AI in dentistry was reported by 83.6% of participants, while 62.1% were aware of its applications in forensic odontology. Only 28.8% had used AI-based tools, mainly for bite mark analysis and age estimation. Most participants (96.3%) believed that AI could improve diagnostic accuracy and efficiency. Ethical and data privacy concerns were identified as major challenges by 76.7% of respondents. Overall, 92.6% demonstrated a positive attitude towards AI integration.
Conclusion: The findings indicate high awareness and acceptance of AI in forensic odontology, although practical utilisation remains limited. Structured training, ethical guidance and regulatory frameworks may support responsible implementation.
Keywords: Artificial intelligence, forensic odontology, dental identification, age estimation, bite mark analysis, disaster victim identification, dental records, machine learning, ethical concerns, data privacy