Attitude, perception and barriers of dental professionals towards artificial intelligence

Medical students and house officers' perception, attitude and potential barriers towards artificial intelligence in Egypt, cross sectional survey

BACKGROUND

Artificial intelligence (AI) is one of the sectors of medical research that is expanding the fastest right now in healthcare. AI has rapidly advanced in the field of medicine, helping to treat a variety of illnesses and reducing the number of diagnostic and follow-up errors.

OBJECTIVE

This study aims to assess the perception and attitude towards artificial intelligence (AI) among medical students & house officers in Egypt.

METHODS

An online cross-sectional study was done using a questionnaire on the Google Form website. The survey collected demographic data and explored participants' perception, attitude & potential barriers towards AI.

RESULTS

There are 1,346 responses from Egyptian medical students (25.8%) & house officers (74.2%). Most participants have inadequate perception (76.4%) about the importance and usage of AI in the medical field, while the majority (87.4%) have a negative attitude. Multivariate analysis revealed that age is the only independent predictor of AI perception (AOR = 1.07, 95% CI 1.01-1.13). However, perception level and gender are both independent predictors of attitude towards AI (AOR = 1.93, 95% CI 1.37-2.74 & AOR = 1.80, 95% CI 1.30-2.49, respectively).

CONCLUSION

The study found that medical students and house officers in Egypt have an overall negative attitude towards the integration of AI technologies in healthcare. Despite the potential benefits of AI-driven digital medicine, most respondents expressed concerns about the practical application of these technologies in the clinical setting. The current study highlights the need to address the concerns of medical students and house officers towards AI integration in Egypt. A multi-pronged approach, including education, targeted training, and addressing specific concerns, is necessary to facilitate the wider adoption of AI-enabled healthcare.

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

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.

Knowledge, attitude, and perception of orthodontic students, and orthodontists regarding role of artificial intelligence in field of orthodontics-An online cross-sectional survey

BACKGROUND

Artificial intelligence (AI) is an emerging technology in orthodontics. The objective of this survey was to evaluate the knowledge, attitude, and perception (KAP) of orthodontists and postgraduate students regarding the plausible employment of AI within the realm of orthodontics.

METHODS

An observational, cross-sectional, online questionnaire survey was conducted with 440 participants (264 postgraduates and 176 faculty members). The questionnaire was divided into four domains: Part A, focused on sociodemographic characteristics, Part B (eight questions) identifying the basic knowledge of the participants about the use of AI in the field of orthodontics, Part C (six questions) assessing the participants' perceptions of the use of AI, and Part D (five questions) assessing the attitudes of participants towards AI. The KAP scores of the participants regarding the use of AI in the field of orthodontics were assessed using a three-point Likert scale for 17 questions and two multiple-choice questions. Responses were analyzed using the chi-square test, Kruskal-Wallis test, and Mann-Whitney test.

RESULTS

A total of 266 participants completed the survey, and the majority agreed with the use of AI in the field of orthodontics, particularly for 3-dimensional diagnosis of orthognathic surgeries, cephalometric analysis, and prediction of treatment outcomes. Most participants felt that AI training should be incorporated into the postgraduate curriculum (73%), and were willing to incorporate it into clinical practice (74%). Barriers to the use of AI were high costs, lack of technical knowledge, and lack of awareness. The participants' KAP scores showed a weak negative correlation with age, years of experience, and designation.

CONCLUSION

The present study concluded that most of the participants were optimistic about the future of AI in orthodontics. Although most orthodontists and postgraduate students had knowledge of AI, there were many barriers to its use in the field of orthodontics.

Attitudes and Perceptions of Dentists and Dental Residents Practicing in the Navi Mumbai Region Toward the Use of Artificial Intelligence in Dentistry: A Descriptive Survey

Introduction Artificial intelligence (AI) has been gaining considerable attention in recent years within the healthcare field. It has established a presence in various aspects of health sciences, including accurate diagnosis and precise, streamlined treatment. This study aimed to assess the attitudes of dental residents and dentists in the Navi Mumbai region toward the use of AI in dentistry. Methods An online questionnaire-based survey was conducted, inviting 130 dental residents and dentists from the Navi Mumbai region. The collected data were compiled on a worksheet and subjected to descriptive statistical tests, which were expressed in numbers and frequencies. Results A total of 100 responses were received. Sixty-eight percent of individuals agreed that AI helps enhance diagnosis and treatment planning in the dental field. Sixty-five percent of the respondents stated that they are most likely to incorporate AI tools into their practice within the next five years. Conclusion From the present study, it can be inferred that AI is a promising and essential subsidiary tool in dentistry as well as in healthcare as a whole. However, major concerns such as extensive, in-depth training, data security, and cybercrime must be addressed before the full-scale incorporation of AI in the health sciences.

Medical, dental, and nursing students' attitudes and knowledge towards artificial intelligence: a systematic review and meta-analysis

BACKGROUND

Nowadays, Artificial intelligence (AI) is one of the most popular topics that can be integrated into healthcare activities. Currently, AI is used in specialized fields such as radiology, pathology, and ophthalmology. Despite the advantages of AI, the fear of human labor being replaced by this technology makes some students reluctant to choose specific fields. This meta-analysis aims to investigate the knowledge and attitude of medical, dental, and nursing students and experts in this field about AI and its application.

METHOD

This study was designed based on PRISMA guidelines. PubMed, Scopus, and Google Scholar databases were searched with relevant keywords. After study selection according to inclusion criteria, data of knowledge and attitude were extracted for meta-analysis.

RESULT

Twenty-two studies included 8491 participants were included in this meta-analysis. The pooled analysis revealed a proportion of 0.44 (95%CI = [0.34, 0.54], P < 0.01, I2 = 98.95%) for knowledge. Moreover, the proportion of attitude was 0.65 (95%CI = [0.55, 0.75], P < 0.01, I2 = 99.47%). The studies did not show any publication bias with a symmetrical funnel plot.

CONCLUSION

Average levels of knowledge indicate the necessity of including relevant educational programs in the student's academic curriculum. The positive attitude of students promises the acceptance of AI technology. However, dealing with ethics education in AI and the aspects of human-AI cooperation are discussed. Future longitudinal studies could follow students to provide more data to guide how AI can be incorporated into education.

Awareness and Approaches Regarding Artificial Intelligence in Dentistry: A Scoping Review

BACKGROUND

Dentistry is one of the unique specialties that deals with both humans and machines. This fact illustrates the strong potential for artificial intelligence (AI) implementation in dentistry, which makes awareness and attitude toward AI an important indicator for the future of this technology in the field. Hence, this scoping review aimed to report the status of awareness and attitude toward AI in dentistry.

METHODOLOGY

To ensure the quality and transparency of the present review, the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) flow chart is reported. Four databases were searched for related topics (Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica database (EMBASE), Google Scholar, and Scopus); 1,430 studies were identified, and after screening and filtering, 21 cross-sectional studies were included.

RESULTS

Twenty-one cross-sectional studies were included and yielded 7,688 participants. With an average level of 50.31% among all the studies that reported awareness (18 studies). Four subgroups' average levels of awareness toward AI in dentistry were reported: 67.16% among dentists, 42.58% among dental students, 45.56% for studies conducted on both dentists and dental students, and 69.53% for studies reporting awareness of AI in oral radiology. Regarding attitude, out of 13 studies, an average level of 44.13% felt threatened or thought AI would replace them.

CONCLUSION

The average level of awareness is in accordance with the attitude toward AI in dentistry. The low levels of awareness are important indicators of the gap formed between the inevitable application of AI and the lack of utilization in the dental field. AI implementation in dental schools' curricula is required since the lowest reported level among subgroups was among dental students.

Knowledge, Attitude and Perception Regarding Artificial Intelligence in Periodontology: A Questionnaire Study

INTRODUCTION

The utilization of artificial intelligence (AI) and machine learning (ML) models has brought about a significant transformation in the manner in which periodontists gather information, evaluate associated risks, develop diverse treatment alternatives, anticipate and diagnose dental conditions that compromise periodontal health. The principal objective of this prospective study was to examine periodontists' understanding and acceptance of the application of AI in the realm of periodontology.

MATERIALS AND METHODS

This observational study was conducted on 275 participants based on questionnaire using Google Forms. These forms were pre-validated and subsequently circulated among periodontists in Maharashtra via various social media platforms. The study, in its entirety, comprised four open-ended questions on participants' demographics and 14 closed-ended questions, all of which were presented to the participants in English. These questions aimed to elicit participants' awareness, knowledge, attitudes, and perspectives regarding emerging applications of AI in the field of periodontology. To analyze the collected data, researchers employed the widely utilized Statistical Package for Social Sciences (SPSS) version 22.0.

RESULT

A 75% response rate was achieved and 68% of the respondents were female. 62% periodontists were aware of AI; however, only 24% were aware of its working principles. Most respondents agreed with the use of AI in periodontal diagnosis; however, they disagreed with the use of AI in predicting clinical attachment loss (69%). 80-82% respondents felt that AI should be a part of postgraduate training and should be implemented in clinical practice. However, most periodontists do not use AI for diagnostic or research purposes. 49% periodontists felt that AI does not have better diagnostic accuracy than periodontists, and therefore cannot replace them in the future.

CONCLUSION

Most periodontists possessed a reasonable level of understanding regarding the utilization of AI in the domain of periodontology and expressed a desire to incorporate it into their diagnostic and treatment planning processes for periodontal conditions. Additional endeavors must be undertaken to enhance periodontists' awareness concerning the effective implementation of AI within their professional practice, with the aim of facilitating personalized treatment planning for their respective patients. It is postulated that the integration of AI will augment the likelihood of achieving favorable outcomes within the realm of periodontology.