Revolutionising dental technologies: a qualitative study on dental technicians' perceptions of Artificial intelligence integration

• Main Authors: Galvin Sim Siang Lin (Author), Yook Shiang Ng (Author), Nik Rozainah Nik Abdul Ghani (Author), Kah Hoay Chua (Author)
• Format: Book
• Published: BMC, 2023-09-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.1186/s12903-023-03389-x; 1472-6831
• Keywords: Artificial intelligence; Computer-aided design; Dental technician; Dental technology; Qualitative study; Dentistry; RK1-715
• Source: BMC Oral Health, Vol 23, Iss 1, Pp 1-8 (2023)

Abstract Background The integration of artificial intelligence (AI) in dentistry has the potential to revolutionise the field of dental technologies. However, dental technicians' views on the use of AI in dental technology are still sparse in the literature. This qualitative study aimed to explore the perceptions of dental technicians regarding the use of AI in their dental laboratory practice. Methods Twelve dental technicians with at least five years of professional experience and currently working in Malaysia agreed to participate in the one-to-one in-depth online interviews. Interviews were recorded, transcribed verbatim and translated. Thematic analysis was conducted to identify patterns, themes, and categories within the interview transcripts. Results The analysis revealed two key themes: "Perceived Benefits of AI" and "Concerns and Challenges". Dental technicians recognised the enhanced efficiency, productivity, accuracy, and precision that AI can bring to dental laboratories. They also acknowledged the streamlined workflow and improved communication facilitated by AI systems. However, concerns were raised regarding job security, professional identity, ethical considerations, and the need for adequate training and support. Conclusion This research sheds light on the potential benefits and challenges associated with the integration of AI in dental laboratory practices. Understanding these perceptions and addressing the challenges can support the effective integration of AI in dental laboratories and contribute to the growing body of literature on AI in healthcare.