Readiness towards artificial intelligence among medical and dental undergraduate students in Peshawar, Pakistan: a cross-sectional survey

Abstract

Introduction

Artificial intelligence is a transformative tool for improving healthcare delivery and diagnostic accuracy in the medical and dental fields. This study aims to assess the readiness of future healthcare workers for artificial intelligence and address this gap by examining students’ perceptions, attitudes, and knowledge related to AI in Peshawar, Pakistan.

Methods

A quantitative cross-sectional survey was conducted on 423 students from randomly chosen medical and dental colleges. The Medical AI Readiness Scale (MAIRS-MS) was used to perform a self-administered online questionnaire that was used to gather data. Using SPSS software, descriptive statistics and chi-square tests were used to evaluate the data. The level of significance was set at p ≤ 0.05.

Results

From multiple medical and dental colleges, 407 students participated in this survey. The survey showed that 29.7% of students had low, 62.2% had moderate, and only 8.1% had high readiness levels. Most medical and dental students in Peshawar, Pakistan, showed moderate readiness. There were significant gender discrepancies, showing males dominating females in readiness scores. There were only slight differences in the AI readiness scores and the academic years from the 1st to 5th year. Only a few non-Pakistani students responded, which may hinder conclusive determinations regarding national disparities.

Conclusion

The study revealed moderate AI readiness among participants, with significant gender disparities favouring males. Overall, there were no significant differences between dentistry and medical fields. In-depth analysis by domain and knowledge areas might uncover further distinctions.

Clinical trial number

Not Applicable.

Background

An AI, artificial intelligence, is a machine that exhibits intelligent behavior, including observation, reasoning, learning, communication, and the capability to execute jobs often done by humans [1]. This technology can revolutionize healthcare delivery in all areas, including medical imaging analysis, diagnostics, personalized treatment, and robotic surgery [2]. It is also utilized in the dental profession for various purposes. These include detecting caries (tooth decay) [3], diagnosing oral cancer [4], selecting a dental shade for the restoration [5], and diagnosing temporomandibular disorders [6]. Currently, there are a few studies [7, 8] that explore or review the existing uses of AI in medical education.

The World Health Organization cautioned that healthcare workers must possess a comprehensive understanding of how AI functions [9]. Currently, it is crucial to ascertain the level of expertise possessed by medical professionals to determine their ability to embrace and utilize emerging technology effectively [10]. The lack of knowledge and doubt regarding AI may hinder its acceptance and potentially result in misapplication. The utilization of AI in healthcare enhances operational efficiency and efficacy while reducing instances of medical negligence [11].

Multiple studies have examined the level of artificial intelligence knowledge and literacy among healthcare providers, including both students and workers [12,13,14] but not many have looked into health professional’s readiness. The Medical Artificial Intelligence Readiness Scale (MAIRS), is a tool for assessing medical professional’s readiness for AI [15]. In-depth research on the instrument and readiness among dental and medical professionals hasn’t been done. In a comprehensive survey [16] conducted across multiple continents, it was found that both medical physicians and students had little knowledge and understanding of artificial intelligence.

The readiness of undergraduate medical and dental students in Peshawar towards artificial intelligence is a topic of growing interest. To our knowledge, limited research studies have been conducted to assess the level of readiness for artificial intelligence among medical and dental students in Pakistan, specifically in Peshawar, using a validated methodology. The objective of this study was to evaluate the level of readiness for artificial intelligence among medical and dentistry students in Peshawar. This research aims to bridge this gap by investigating students’ perceptions, attitudes, knowledge, and skills regarding AI in Peshawar.

Methods

Study design & setting

This quantitative cross-sectional descriptive study collected the participants’ responses to an already validated data collection instrument. The multi-stage sampling method was employed to select participants. In the first stage, out of twenty medical colleges and nine dental colleges, two medical colleges and one dental college (public & private) in Peshawar were chosen randomly. Both public and private sector colleges, including Khyber Medical College, Khyber Girls Medical College, Kabir Medical College, Rehman Medical College, Khyber College of Dentistry, and Sardar Begum Dental College, were included in the study. In the second stage, Proportion for each college was determined using n/N×SS, Where n = the total number of students in the college, N = total number of students in all the colleges, and SS = sample size. After that, students per year were determined and taken randomly according to the number of students enrolled shown in Table 1.

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Study participants

The multi-stage sampling method was employed to select participants, and the study aimed to bridge this gap by investigating students’ perceptions, attitudes, knowledge, and skills regarding AI in Peshawar. The target population was selected by calculating the number of students required in all Peshawar colleges (Table 1). OpenEPi was used to calculate the sample size needed, aiming for a 95% confidence level with a 5% margin of error. The sample size was 384. Adding 10% (n = 39) to the sample size for non-responders makes the total sample size 423. All the students enrolled in undergraduate medical and dental degree programs who were willing to participate were included. Students currently on leave and with cognitive impairments were excluded.

Research instrument

A preformed and validated questionnaire was used to collect the data [15]. The study instrument was tested beforehand and considered reliable with Cronbach’s Alpha score α = 0.73. No question was added, subtracted, or changed in the questionnaire. The questionnaire assessed the knowledge, skills, attitudes, and ethical considerations regarding AI in healthcare and was administered in English. It consisted of 30 questions, which were separated into two portions. Section One gathered demographic information, encompassing gender, age, city of residence, field of expertise, educational institution attended, qualifications, and nationality. Section Two evaluated the readiness of artificial intelligence by utilizing a set of 22 questions that were responded to on a Likert Scale with five points, ranging from 1 (indicating strong disagreement) to 5 (indicating strong agreement).

Data collection procedure

After approval from the ethical board (Ref No: 1–12/IHPER/MHPE/KMU/23 − 11) and college administrations, the questionnaire was distributed as Google form links via email and online platforms (WhatsApp) accessible to students. Paper-based versions of the MAIRS-MS were given to students without the virtual facility. Each Google Form consisted of an information sheet, a consent form, and a questionnaire called The Medical AI Readiness Scale– Medical Students (MAIRS-MS) which is a preformed validated questionnaire (α = 0.73) [15]. Reminder emails/announcements were sent through college communication channels to maintain engagement. All the entries were gathered on an Excel spreadsheet.

Data analysis procedure

Data was analyzed using Statistical Package of Social Sciences (SPSS version 26) software. Statistical significance was kept at P < 0.05. Participant characteristics and AI readiness scores summarized descriptive statistics. Data was normally distributed. Means and SD were taken out for continuous variables (e.g., AI readiness scores for students), and frequency and percentages (e.g., year of study for students) were calculated for the categorical variables. Group comparisons (e.g., dental vs. medical students) were performed using suitable statistical tests (e.g., Chi-square, ANOVA).

Results

Demographic parameters

A total of 407 students answered the questionnaire out of 424 students, including 343 medical and 64 dental students giving a 96% response rate (Fig. 1). The number varied among the colleges, as the number of students differed in each college from 1st to 5th year .

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Twenty Medical and Nine Dental Colleges offer Bachelor of Medical (MBBS) and Dental Surgery (BDS) degree programs in Peshawar. Four Medical and Two Dental colleges were included in this study. The responses collected were not equal. As the number of students enrolled in each college varies, so does the response rate. The following chart explains the results obtained from different colleges. Khyber Medical College had the highest response, as more students were enrolled in this college, hence the larger sample size. Khyber College of Dentistry and Sardar Begum Dental College had the lowest response. Dentistry-enrolled seats are lower compared to medical seats. Hence, the lower sample size leads to lower response rates (Fig. 2).

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There were 166 Males and 241 Females who responded to the questionnaire. 59% of the respondents were females and 41% were males. The increase in female respondents is because one of the random colleges was the only female medical college, hence the reason. The mean age of the study’s 407 participants is 21.47. The standard deviation of 1.744 shows a moderate spread in the students’ ages. Most students are within a few years of the average age (Table 2).

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According to their college year, 84 students responded from the first year, 86 from the second year, 85 from the third year, 83 from the fourth year, and only 69 from the fifth year. The reason for fewer respondents in the fifth year is that two were dentistry colleges, and there are just four years of academics in dentistry. However in Nationality, 397 respondents were Pakistani, and only 10 were non-Pakistani nationals. This shows that only a limited number of non-nationals are studying in Peshawar at the time of the survey.

Readiness parameters

According to the Readiness level, 121 students responded with a low level of readiness, making up 30% of the total respondents, and 253 responded with a moderate level of readiness, making up 62% of the total respondents. In contrast, only 33 students responded to having a high level of readiness, making up 8% of respondents (Fig. 3). The results show that the majority of the students have a moderate level, whereas a minority of the students have a high level of readiness. However, still, a large number of students have low levels, stating they are still not ready for artificial intelligence in the 21st century .

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Table 3 shows the frequency and percentage distribution of participants within each gender across different levels of AI readiness: low, moderate, and high. When readiness is analyzed across different genders, Male and female students show significant differences in readiness levels. A higher percentage of males reported moderate and high readiness levels than females. In contrast, females reported a higher rate of low-level readiness than the male gender. It is also suggested in Table 3 that 62.2% of participants showed a moderate level of readiness, 29.7% showed a low level, and only 8.1% showed a high level of readiness. This states that students in Peshawar undergoing medical and dental education are generally moderately ready for artificial intelligence.

It also represents the chi-square test results, determining the association between gender and AI readiness level. The Pearson Chi-Square test indicates a statistically significant association between gender and AI readiness level. The p-value was 0.002. The value of 12.872 suggests a moderate to strong association between gender and AI Readiness. These results indicate that gender significantly predicts artificial Intelligence readiness among students. Males represent a higher level of readiness compared to females.

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Table 4 illustrates the Specialty’s frequency and percentage distribution of participants across various levels of AI readiness. Analysis reveals that medical students demonstrated more moderate degrees of AI readiness than their dental peers. Specifically, 62.7% of medical students exhibited moderate readiness, whereas only 59.4% of dental students did. In contrast, a greater percentage of dental students indicated high AI readiness (10.9%) than medical students (7.6%). The dental and medical students had the same Low readiness percentage (29.7%). These findings indicate a lack of statistically significant correlation between medical or dental speciality and AI readiness among the study participants.

Students at Khyber Medical College demonstrated the highest levels of preparation among all colleges, with low, moderate, and high readiness levels, as shown in Table 5. The majority of respondents are from KMC. Many students in all colleges demonstrated moderate readiness.

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The results indicate no statistically significant (p = 0.526) association between the study participants’ college affiliation and AI readiness (Table 5). Students from various colleges had similar knowledge, skills, and attitudes regarding AI.

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There was no statistically significant relationship between the academic year and the level of AI readiness among the study participants, with a p-value of 0.144. Students from various academic years exhibited comparable readiness levels (Table 6).

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Students of Pakistani origin and other origins demonstrated comparable percentages in the low, moderate, and high AI readiness levels, showing only slight differences. The results indicate no statistically significant (p-value = 0.431) relationship between nationality and AI readiness among the study participants, as shown in Table 7.

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Table 8 reveals no statistical significance between academic year, gender, and AI readiness levels. This suggests that the effects of gender and academic year on AI readiness are independent of each other. The overall total value of all the student’s academic year, gender, and AI readiness scores were highly significant (p-value = 0.002). This can be because, in the third academic year, gender and AI readiness scores were significant (p-value = 0.036). The possible reasons for this are consistent gender disparities, curriculum factors, and student characteristics. Previous analyses indicate that gender is a notable predictor of AI readiness, with male students exhibiting higher levels than their female counterparts. Although there were slight variations in AI readiness levels over the years of study, these discrepancies were not statistically significant.

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Table 9 shows the means and standard deviation of different AI readiness Factor Scores. The findings indicate that students possess a solid grasp of AI concepts and their applications (Ability factor), yet they appear to have restricted knowledge or skills in aspects like visualizing and interpreting AI outputs (Vision factor). Furthermore, although their grasp of ethical considerations in AI is reasonable, there is room for improvement.

Discussion

The study was conducted to assess the level of readiness for artificial intelligence among medical and dental professionals, particularly in Peshawar, using a validated MAIR-MS tool. Their readiness was assessed using the aggregate scores attained by the students on the validated tool, which encompassed four domains: cognitive, ability, vision, and ethics. A higher score signified greater agreement with the survey questionnaire statements and an elevated level of readiness toward AI among undergraduate medical and dental students. The primary purpose of this study was to determine the readiness of upcoming healthcare professionals for AI, which would help us devise possible solutions for implementing this new technology in daily life.

This research demonstrated that Pakistan’s medical and dental students (97.3%) exhibited moderate readiness for AI (62.2%), with lesser levels at 29.7% and higher levels at 8.1%. Male students exhibited a significantly higher level of AI readiness than their female counterparts. No significant differences were observed between the medical and dental disciplines or across different academic years. Numerous studies have repeatedly identified disparities in AI readiness between genders. It is suggested that male students typically exhibit more outstanding expertise and excitement for AI than their counterparts. Our research findings yielded identical outcomes.

Various variables may contribute to these gender discrepancies, including societal expectations, educational backgrounds, and access to technology. Societal preconceptions and expectations can impede girls and women from pursuing jobs in technology and science, leading to diminished exposure to AI and other disciplines. Additionally, male and female students may have different educational experiences and opportunities to learn about AI, with males potentially having better access to relevant courses, resources, and mentorship. Cultural variables and gender norms can influence attitudes toward innovation and technology. Women may be discouraged from engaging in AI due to perceptions that associate technology with masculinity [17], and their access to education and employment possibilities in the technology sector may be limited by societal norms that prioritize traditional gender roles.

A study conducted in Beijing in 2020 by Yun Dai et al. revealed that male students reported higher relevance, confidence, and readiness for AI compared to female students [18]. In a study conducted by Dos Santos et al., male participants exhibited greater confidence in utilizing AI applications and demonstrated reduced apprehension toward AI technologies [14]. A notable distinction regarding gender and the integration of AI into clinical practice was identified in another investigation examining physician perspectives on AI’s role in diagnostic pathology conducted by Sarwar in Canada. The study found that males exhibited greater comfort in working with computer science technology compared to females [19]. These findings reinforce our study’s results, emphasizing the need for interventions to address gender disparities in AI education and technology adoption.

Contrary to our findings, a study conducted in Malaysia showed that gender was not significantly associated with AI readiness [20]. Another cross-sectional survey showed no gender differences in student’s AI literacy [18]. The Medical Artificial Intelligence Readiness Scale (MAIRS) did not reveal any substantial disparities in AI readiness between male and female students in a study by Halat et al. [21]

There have been mixed findings in studies about the main differences between dental and medical students when it comes to the readiness for artificial intelligence. While some research suggests that dentists may be better equipped to deal with AI, other studies have not shown any significant variations. The varying outcomes of the readiness of medical and dental students for AI could originate from multiple causes. Various study designs, sample sizes, and data collection techniques can influence the outcomes. Moreover, several studies may have exclusively examined the readiness of the two groups for AI, whereas others may have pursued broader objectives. Differences in culture, education, and society between regions can also affect how ready healthcare students are for AI. This could be due to a similar curriculum regarding AI content, shared experiences, and equal exposure to AI. These things show that more study is needed to better understand how medical and dental professionals might be different in their readiness for AI.

Researchers have encountered inconsistent results while assessing the AI readiness of dental and medical students. In comparison to medical professionals, dental professionals in Saudi Arabia exhibited a higher degree of readiness for AI integration [22]. Our findings are consistent with the research, which indicates that dental students are more prepared for AI. Conversely, medical students are more moderately prepared for AI. This may be a consequence of the dental profession’s growing dependence on machinery and technical expertise in practice. Dentists have adopted recent technological advancements, including 3D printing, scanning, and digital radiographs [23,24,25,26]. Another study conducted in Malaysia matches our findings, which suggest intermediate preparation for medical students for artificial intelligence [20]. The absence of satisfactory readiness indicates that medical and dental professionals lack the necessary knowledge and preparedness to implement AI. The disparity must be addressed, as it is essential in light of the transformative impact of AI on the future of healthcare. This will require substantial effort [2, 27]. In contrast, dental students exhibit greater preparedness compared to their counterparts regarding AI readiness scores. According to another researcher, a comparison of the two groups revealed no statistically significant differences [28]. Numasawa’s research indicates that dental students are less equipped for interdisciplinary learning than their colleagues in medicine and nursing [29].

The literature on AI readiness among medical students indicates that various factors can influence their readiness for AI integration in healthcare. These criteria include the year of study, prior AI experience, and the curriculum’s emphasis on AI-related topics. Various elements determine AI readiness and the year of study. Prior AI training and an emphasis on AI-related themes in medical curriculum are all important considerations. Furthermore, curriculum consistency across years may help students achieve a more uniform degree of AI preparation. As students continue through their medical education, their AI skills may gradually improve. Furthermore, shared experiences and exposure to AI-related concepts throughout the academic curriculum may have contributed to students’ common understanding and preparation for AI in our study.

A study carried out in Malaysia established a significant association between study years and AI readiness in medical students, specifically [20]. However, our study did not show any significant difference between the academic year and the AI readiness score. A prior investigation revealed that students expressed concerns regarding the insufficiency of their medical education in equipping them to collaborate effectively with AI tools or applications. The consensus was that additional preparation within the medical program was essential to enhance their AI readiness level [30].

Studies on AI readiness indicate notable differences among countries. An index assessing AI capabilities and readiness across 80 countries indicates that the USA, China, Japan, and South Korea are at the forefront. In contrast, numerous countries in Africa, Asia, and Latin America are significantly behind [31]. In developing countries such as Palestine, the low awareness of AI and its limited application across various sectors underscore the necessity for focused national strategies [32]. In advanced economies such as Saudi Arabia, healthcare professionals exhibit low levels of AI readiness, indicating a necessity for enhanced education and training [22].

This study revealed that Pakistani and non-Pakistani students showed comparable results, which were statistically insignificant. The reason behind this could be the similarities in the students’ experiences and the influence of shared aspects with a lack of diversity from individuals outside Pakistan. It could limit the capacity to identify meaningful variations among nationalities. The limited number of students from outside Pakistan may introduce bias and necessitate additional research in the future to thoroughly evaluate their connection.

Readiness for Artificial Intelligence across sectors, especially in healthcare and education, involves four critical factors: cognition, ability, vision, and ethics [15, 33, 34]. These factors are essential for medical students and educators in their fields to effectively integrate AI. Research has established and validated instruments to assess AI readiness, including the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) [15] and comparable tools for educators [34].

It is necessary to take a holistic view of dental and medical students’ AI readiness by investigating their skills, ethics, vision, and cognitive processes. If students can develop a strong cognitive foundation in AI and demonstrate proficiency in using AI tools and applications, they are more prepared for the integration of AI. An in-depth understanding of ethical concerns, as well as the ability to visualize and evaluate artificial intelligence results, are equally vital. Addressing all four criteria can improve medical institutions’ ability to better equip students to use artificial intelligence to improve healthcare outcomes. Our study indicates a moderate level of readiness in both medical and dental students.

Limitations of the study

This study identifies several limitations that should be considered when interpreting the findings. The restricted sample size, particularly concerning non-Pakistani students, may limit the applicability of the findings. Relying on self-reported data can result in biases, such as social desirability bias. The study’s cross-sectional design limited the evaluation of changes in AI readiness over time. The identified limitations may impede the study’s ability to comprehensively assess AI readiness among medical and dental students in Peshawar.

Future research

Future research should consider several areas to solve the limitations of this study and provide a more thorough understanding of medical and dentistry students’ preparedness for AI. Expanding the research sample sizes, especially by incorporating more non-Pakistani students, can improve the findings’ representativeness. A more excellent knowledge of AI readiness characteristics and their influence can be obtained using mixed methods research, combining quantitative and qualitative methodologies. Studies that follow participants’ improvements in AI readiness over time can evaluate the efficacy of interventions and spot new patterns. Furthermore, contrasting the degree of AI preparation among students in various parts of Pakistan and with those in other nations might shed light on regional differences and international best practices.

Recommendations

The results of this research have implications for decision-makers and educational organizations in Pakistan for a focused effort to prepare dental and medical students for the integration of AI technologies into their curriculum. Providing adequate training and resources for faculty members to practically engage students with AI tools while focusing on ethical aspects, promoting gender equality, and fostering an innovation culture through national programs. The suggestions are intended to allow healthcare workers to effectively use AI to improve care and healthcare results in Pakistan.

Conclusion

The findings showed that the participants’ level of AI readiness is moderate. There were notable disparities in AI preparation across genders, with males showing much more readiness than females. The dentistry and medical fields did not differ significantly. However, additional research into certain domains of expertise and areas of knowledge pertaining to AI within each field may reveal complex differences. Modifications to the curriculum are required due to small differences in AI readiness levels between different academic years.

Data availability

Data is not shared publicly because permission for open public access has not been granted. It is safely stored on a server. But it is available from the corresponding author upon reasonable request.

Abbreviations

ASRB:

Advanced Studies Research Board

ERIC:

Educational Resources Information Center

IHPER:

Institute of Health Profession Education and Research

KPK:

Khyber Pakhtunkhwa

BDS:

Bachelor of Dental Surgery

MeSH:

Medical Subject Headings

KMU:

Khyber Medical University

KMC:

Khyber Medical College

KGMC:

Khyber Girls Medical College

RMC:

Rehman Medical College

KCD:

Khyber College of Dentistry

SBDC:

Sardar Begum Dental College

AI:

Artificial Intelligence

References

Nilsson NJ. Artificial intelligence: A new synthesis. San Francisco: Morgan Kaufmann Publishers, Inc; 1998.

Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):230–43.

Lee JH, Kim DH, Jeong SN, Choi SH. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent [Internet]. 2018;77(June):106–11. Available from: https://doi.org/10.1016/j.jdent.2018.07.015

Ariji Y, Fukuda M, Kise Y, Nozawa M, Yanashita Y, Fujita H et al. Contrast-enhanced computed tomography image assessment of cervical lymph node metastasis in patients with oral cancer by using a deep learning system of artificial intelligence. Oral Surg Oral Med Oral Pathol Oral Radiol [Internet]. 2019;127(5):458–63. Available from: https://doi.org/10.1016/j.oooo.2018.10.002

Wei J, Peng M, Li Q, Wang Y. Evaluation of a novel computer color matching system based on the improved Back-Propagation neural network model. J Prosthodont. 2018;27(8):775–83.

Bas B, Ozgonenel O, Ozden B, Bekcioglu B, Bulut E, Kurt M. Use of artificial neural network in differentiation of subgroups of temporomandibular internal derangements: A preliminary study. J Oral Maxillofac Surg [Internet]. 2012;70(1):51–9. Available from: https://doi.org/10.1016/j.joms.2011.03.069

Lillehaug S-I, Lajoie,Susanne P. AI in medical education - another grand challenge for medical informatics. Artif Intell Med. 1998;12(3):197–225.

Wartman SA, Donald Combs C. Medical education must move from the information age to the age of artificial intelligence. Acad Med. 2018;93(8):1107–9.

Guidance WHO. Ethics and Governance. 2021. 3–22 p.

Kolachalama VB, Garg PS. Machine learning and medical education. npj Digit Med [Internet]. 2018;1(1):2–4. Available from: https://doi.org/10.1038/s41746-018-0061-1

Meskó B, Hetényi G, Gyorffy Z. Will artificial intelligence solve the human resource crisis in healthcare? BMC Health Serv Res. 2018;18(1):1–4.

Sit C, Srinivasan R, Amlani A, Muthuswamy K, Azam A, Monzon L, et al. Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey. Insights Imaging. 2020;11(1):7–12.

Gong B, Nugent JP, Guest W, Parker W, Chang PJ, Khosa F, et al. Influence of artificial intelligence on Canadian medical students’ preference for radiology specialty: anational survey study. Acad Radiol. 2019;26(4):566–77.

Pinto dos Santos D, Giese D, Brodehl S, Chon SH, Staab W, Kleinert R, et al. Medical students’ attitude towards artificial intelligence: a multicentre survey. Eur Radiol. 2019;29(4):1640–6.

Karaca O, Çalışkan SA, Demir K. Medical artificial intelligence readiness scale for medical students (MAIRS-MS)– development, validity and reliability study. BMC Med Educ. 2021;21(1):1–9.

Boillat T, Nawaz FA, Rivas H. Readiness to embrace artificial intelligence among medical Doctors and students: Questionnaire-Based study. JMIR Med Educ. 2022;8(2):e34973.

Armutat S, Wattenberg M, Mauritz N. Artificial intelligence: Gender-Specific differences in perception, understanding, and training interest. Proc Int Conf Gend Res. 2024;7(1):36–43.

Dai Y, Chai CS, Lin PY, Jong MSY, Guo Y, Qin J. Promoting students’well-being by developing their readiness for the artificial intelligence age. Sustain. 2020;12(16):1–15.

Sarwar S, Dent A, Faust K, Richer M, Djuric U, Van Ommeren R et al. Physician perspectives on integration of artificial intelligence into diagnostic pathology. npj Digit Med [Internet]. 2019;2(1):1–7. Available from: https://doi.org/10.1038/s41746-019-0106-0

Xuan PY, Fahumida MIF, Hussain MIAN, Jayathilake NT, Khobragade S, Soe HHK, et al. Readiness towards artificial intelligence among undergraduate medical students in Malaysia. Educ Med J. 2023;15(2):49–60.

Hammoudi Halat D, Shami R, Daud A, Sami W, Soltani A, Malki A. Artificial intelligence readiness, perceptions, and educational needs among dental students: A Cross-Sectional study. Clin Exp Dent Res. 2024;10(4):1–11.

Aboalshamat K, Alhuzali R, Alalyani A, Alsharif S, Qadhi H, Almatrafi R, et al. Medical and dental professionals readiness for artificial intelligence for Saudi Arabia vision 2030. Int J Pharm Res Allied Sci. 2022;11(4):52–9.

Tian Y, Chen CX, Xu X, Wang J, Hou X, Li K et al. A Review of 3D Printing in Dentistry: Technologies, Affecting Factors, and Applications. Scanning. 2021;2021.

Lin L, Fang Y, Liao Y, Chen G, Gao C, Zhu P. 3D printing and digital processing techniques in dentistry: A review of literature. Adv Eng Mater. 2019;21(6):1–28.

Javaid M, Haleem A, Kumar L. Current status and applications of 3D scanning in dentistry. Clin Epidemiol Glob Heal [Internet]. 2019;7(2):228–33. Available from: https://doi.org/10.1016/j.cegh.2018.07.005

Elhelbawy NG, Ghouraba RF, Hasaneen FA. A Comparative Evaluation of the Radiopacity of Contemporary Restorative CAD/CAM Blocks Using Digital Radiography Based on the Impact of Material Composition. Int J Biomater. 2022;2022.

Wen Z, Huang H. The potential for artificial intelligence in healthcare. J Commer Biotechnol. 2022;27(4):217–24.

Bisdas S, Topriceanu CC, Zakrzewska Z, Irimia AV, Shakallis L, Subhash J et al. Artificial intelligence in medicine: A multinational Multi-Center survey on the medical and dental students’ perception. Front Public Heal. 2021;9(December).

Numasawa M, Nawa N, Funakoshi Y, Noritake K, Tsuruta J, Kawakami C et al. A mixed methods study on the readiness of dental, medical, and nursing students for interprofessional learning. PLoS One [Internet]. 2021;16(7 July):1–12. Available from: https://doi.org/10.1371/journal.pone.0255086

Mehta N, Harish V, Bilimoria K, Morgado F, Ginsburg S, Law M, et al. Knowledge and attitudes on artificial intelligence in healthcare: A provincial survey study of medical students. MedEdPublish. 2021;10(1):1–14.

Porcher S. Measuring Artificial Intelligence Capabilities and Readiness. Acad Manag Annu Meet Proc [Internet]. 2020; Available from: https://journals.aom.org/doi/https://doi.org/10.5465/AMBPP.2020.13168abstract

Demaidi MN. Artificial intelligence national strategy in a developing country. AI Soc [Internet]. 2023;(0123456789). Available from: https://doi.org/10.1007/s00146-023-01779-x

Rezazadeh H, Ahmadipour H, Salajegheh M. Psychometric evaluation of Persian version of medical artificial intelligence readiness scale for medical students. BMC Med Educ. 2023;23(1):1–7.

Wang X, Li L, Tan SC, Yang L, Lei J. Preparing for AI-enhanced education: Conceptualizing and empirically examining teachers’ AI readiness. Comput Hum Behav [Internet]. 2023;146:107798. Available from: https://api.semanticscholar.org/CorpusID:258368786