Attitudes and Perceptions of Australian Dentists and Dental Students Towards Applications of Artificial Intelligence in Dentistry: A Survey.

IF 1.7 4区 教育学 Q3 DENTISTRY, ORAL SURGERY & MEDICINE European Journal of Dental Education Pub Date : 2024-09-28 DOI:10.1111/eje.13042
Shwetha Hegde, Shanika Nanayakkara, Ashleigh Jordan, Omar Jeha, Usaamah Patel, Vivian Luu, Jinlong Gao
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Abstract

Introduction: As artificial intelligence (AI) rapidly evolves in dentistry, understanding dentists' and dental students' perspectives is key. This survey evaluated Australian dentists' and students' attitudes and perceptions of AI in dentistry.

Methods: An online questionnaire developed on Qualtrics was distributed among registered Australian dentists and students enrolled in accredited Australian dental or oral health programmes. Descriptive and bivariate analyses were used to examine the demographic variables and participant attitudes.

Results: 177 responses were received, and 155 complete responses were used in data analysis. 54.8% were aware of dental AI applications, but 70.3% could not name a specific AI software. A majority (91.6%) viewed AI as a supportive tool, with 69% believing that it would be beneficial in clinical tasks and 35.6% expecting it to perform similarly to an average specialist. 40% anticipated that dental AI would be routinely used in the next 5-10 years, with more dental students expecting this short-term integration. Concerns included job displacement, inflexibility in patient care, and mistrust of AI's accuracy. Attitudes towards AI were influenced by age, gender, clinical experience and technological proficiency.

Conclusions: The survey underscores the potential of AI to revolutionise dental care, enhancing clinical workflows and decision-making. However, challenges like trust in AI and ethical concerns remain. It is recommended that practising dentists receive hands-on training with AI tools and continuing dental education programmes. Integrating AI into dental curricula and fostering interdisciplinary teaching and research collaborations between computer science and dentistry is necessary to prepare graduates to use AI effectively and responsibly.

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澳大利亚牙医和牙科学生对人工智能在牙科应用的态度和看法:调查。
导言:随着人工智能(AI)在牙科领域的快速发展,了解牙医和牙科学生的观点至关重要。这项调查评估了澳大利亚牙医和学生对牙科人工智能的态度和看法:方法:在Qualtrics上开发了一份在线调查问卷,向注册的澳大利亚牙医和注册澳大利亚牙科或口腔健康课程的学生发放。采用描述性分析和双变量分析来研究人口统计学变量和参与者的态度:结果:共收到 177 份回复,其中 155 份完整回复用于数据分析。54.8%的人知道牙科人工智能应用,但70.3%的人说不出具体的人工智能软件。大多数人(91.6%)认为人工智能是一种辅助工具,69%的人认为人工智能将有益于临床工作,35.6%的人期望人工智能的表现与普通专科医生相似。40%的人预计人工智能将在未来5-10年内得到常规使用,更多的牙科学生期待这种短期整合。担心的问题包括工作被取代、病人护理缺乏灵活性以及对人工智能准确性的不信任。对人工智能的态度受年龄、性别、临床经验和技术熟练程度的影响:这项调查强调了人工智能在革新牙科护理、改进临床工作流程和决策方面的潜力。然而,对人工智能的信任和伦理问题等挑战依然存在。建议执业牙医接受人工智能工具的实践培训和继续牙科教育课程。将人工智能纳入口腔医学课程,促进计算机科学与口腔医学之间的跨学科教学与研究合作,对于培养毕业生有效、负责任地使用人工智能十分必要。
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来源期刊
CiteScore
4.10
自引率
16.70%
发文量
127
审稿时长
6-12 weeks
期刊介绍: The aim of the European Journal of Dental Education is to publish original topical and review articles of the highest quality in the field of Dental Education. The Journal seeks to disseminate widely the latest information on curriculum development teaching methodologies assessment techniques and quality assurance in the fields of dental undergraduate and postgraduate education and dental auxiliary personnel training. The scope includes the dental educational aspects of the basic medical sciences the behavioural sciences the interface with medical education information technology and distance learning and educational audit. Papers embodying the results of high-quality educational research of relevance to dentistry are particularly encouraged as are evidence-based reports of novel and established educational programmes and their outcomes.
期刊最新文献
The Graduating European Dentist Curriculum Framework: A 7-Year Review. Beyond the Drill: Understanding Empathy Among Undergraduate Dental Students. Future-Proofing Dentistry: A Qualitative Exploration of COVID-19 Responses in UK Dental Schools. Mapping the Landscape of Generative Language Models in Dental Education: A Comparison Between ChatGPT and Google Bard. Performance of a Generative Pre-Trained Transformer in Generating Scientific Abstracts in Dentistry: A Comparative Observational Study.
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