Skip to main content
Download PDF

Shaping the future: perspectives on the Integration of Artificial Intelligence in health profession education: a multi-country survey

BMC Medical Education volume 24, Article number: 1166 (2024) Cite this article

Abstract

Background

Artificial intelligence (AI) is transforming health profession education (HPE) through personalized learning technologies. HPE students must also learn about AI to understand its impact on healthcare delivery. We examined HPE students’ AI-related knowledge and attitudes, and perceived challenges in integrating AI in HPE.

Methods

This cross-sectional included medical, nursing, physiotherapy, and clinical nutrition students from four public universities in Jordan, the Kingdom of Saudi Arabia (KSA), the United Arab Emirates (UAE), and Egypt. Data were collected between February and October 2023 via an online survey that covered five main domains: benefits of AI in healthcare, negative impact on patient trust, negative impact on the future of healthcare professionals, inclusion of AI in HPE curricula, and challenges hindering integration of AI in HPE.

Results

Of 642 participants, 66.4% reported low AI knowledge levels. The UAE had the largest proportion of students with low knowledge (72.7%). The majority (54.4%) of participants had learned about AI outside their curriculum, mainly through social media (66%). Overall, 51.2% expressed positive attitudes toward AI, with Egypt showing the largest proportion of positive attitudes (59.1%). Although most participants viewed AI in healthcare positively (91%), significant variations were observed in other domains. The majority (77.6%) supported integrating AI in HPE, especially in Egypt (82.3%). A perceived negative impact of AI on patient trust was expressed by 43.5% of participants, particularly in Egypt (54.7%). Only 18.1% of participants were concerned about the impact of AI on future healthcare professionals, with the largest proportion from Egypt (33.0%). Some participants (34.4%) perceived AI integration as challenging, notably in the UAE (47.6%). Common barriers included lack of expert training (53%), awareness (50%), and interest in AI (41%).

Conclusion

This study clarified key considerations when integrating AI in HPE. Enhancing students’ awareness and fostering innovation in an AI-driven medical landscape are crucial for effectively incorporating AI in HPE curricula.

Peer Review reports

Introduction

The concept of artificial intelligence (AI) has evolved over several decades. It emerged to navigate ways that computers, computer-controlled robots, or software think like human minds and exhibit intelligent behaviors equivalent to those of human beings [1]. AI is a broad field encompassing the science and engineering of making intelligent machines. Various AI applications have been used to provide effective solutions to complicated challenges across different fields and disciplines [1]. AI comprises two main overlapping technological subfields: machine learning and deep learning. Machine learning is based on the idea that computers can learn from data and algorithms, identify data-driven patterns, and make classifications, predictions, and decisions with minimal human intervention. Deep learning is part of machine learning and is based on layering algorithms and data into “artificial and deep neural networks” that resemble the human brain to allow machines to perform increasingly complex tasks and achieve state-of-the-art performance in various domains without human intervention [1]. Although machine learning and deep learning are often used interchangeably, deep learning is generally considered more advanced and sophisticated [1].

Similar to other fields and industries, AI is widely recognized in healthcare as a ground-breaking technological innovation that leads to better treatment outcomes [2,3,4,5]. The scope of applications of AI in healthcare is enormous and includes AI-led drug discovery and its clinical relevance, robotics in surgery, virtual health assistants and chatbots, genomic analysis, predictive analytics, and medical imaging analysis [6]. The advantages of using AI in healthcare include early disease detection and diagnosis, personalized medicine, improved workflow, decreased workload, reduced costs, and improved overall efficiency in healthcare systems globally [7,8,9].

The rapid encroachment of AI and its impact on healthcare has resulted in educational and healthcare leaders placing pressure on academic institutions to integrate AI applications in health profession education (HPE). This aims to equip future healthcare professionals to effectively navigate AI applications and function within intelligent healthcare systems. There is also growing research and development enthusiasm about embedding AI applications in HPE curricula. Recent studies showed AI readiness and acceptance among educators, who viewed AI as an adaptive learning tool that could relieve them of monotonous tasks and assist in providing constructive and individual feedback to students based on their individual learning needs [10, 11]. Similarly, students reported AI technology aided them in receiving specialized assistance from educators and identifying learning needs and knowledge gaps [11]. Importantly, there is consensus across studies that introducing AI in HPE is associated with preparing competent healthcare professionals, and leads to the retention of information and development of independent, life-long learning, problem-solving, and clinical reasoning skills [10, 12]. A recent study from the United Arab Emirates (UAE) involving first-year medical students (N = 61) examined the implementation of AI-based simulation to assess non-psychomotor skills, including clinical decision-making and communication [13]. The results demonstrated AI was a highly effective and acceptable assessment tool for both educators and students [13]. Given the accelerated integration of AI in healthcare, it is essential that healthcare professionals are adequately trained in this new technology and graduates are prepared for AI early during their studies [14,15,16]. Training in AI concepts must therefore be integrated into undergraduate HPE curricula to ensure students are exposed to AI early so they can navigate its application during practice [15].

Despite enthusiasm about integrating AI in HPE and the increased amount of research on this topic, there is a lack of basic literacy and understanding of AI technology among HPE students [6, 17,18,19,20]. This study aimed to respond to this identified lack of AI literacy, enhance awareness about AI in healthcare, and leverage the integration of AI into HPE by assessing knowledge, attitudes, and barriers related to the integration of AI in HPE among students in Arabic countries [21]. This study is expected to contribute to establishing evidence to address the knowledge gap related to integrating AI in HPE. The findings will support educational leaders to engage in building appropriate resources and infrastructure, promote international collaboration, and aid in disseminating information about AI and HPE in the Arab region.

Aims and objectives

This study used a sample of HPE students from four public universities in four Arab countries: Jordan, the UAE, the Kingdom of Saudi Arabia (KSA), and Egypt. The specific aims of this study were as follows.

  1. 1.

    To assess students’ basic knowledge and training in AI, and sources of AI-related knowledge.

  2. 2.

    To assess students’ attitudes toward the integration of AI in HPE (including perceived benefits of AI in healthcare, negative impact on patient trust, negative impact on the future of healthcare professionals, potential inclusion of AI in HPE curricula, and challenges hindering AI integration).

  3. 3.

    To assess students’ perceptions of barriers associated with integrating AI in HPE.

Methods

Research design and setting

This cross-sectional study recruited HPE students from four public universities in Jordan, the UAE, the KSA, and Egypt. Data were collected using a self-administered, web-based survey. Data collection was conducted from February to October 2023.

Recruitment and sampling

Participant recruitment for this study was conducted using non-probability convenience sampling. The sample included 642 medical and health sciences students from four neighboring Arab countries (Jordan, the UAE, the KSA, and Egypt). The researchers selected one large public university from each country that had medicine and other health-related programs. Public universities were selected as they were considered likely to be responsive to governmental educational policies and technological integration. These universities are also required to respond to mandates based on national needs. Selection criteria for universities were being a locally accredited well-reputed public university that offered medicine and health sciences specialties (e.g., nursing, physiotherapy, and nutritional sciences) and willing to participate in this study. The inclusion of healthcare specialties beyond medicine was essential to gain in-depth insights regarding the integration of AI in HPE, as most available studies focused on medical students’ perspectives. Therefore, our results can be used to inform collective curricula development, unify AI training protocols, and promote interdisciplinary and interprofessional educational initiatives relating to integrating AI in HPE.

The sample size was calculated using Raosoft, which is a valid sample size calculator (http://www.raosoft.com/samplesize.html). Our target population was undergraduate HPE students from the four selected universities. With an estimated population size of 20,000 (note: the exact number of students was unknown), and a 5% margin error with 95% confidence interval, the required sample size was 384 students. Based on an estimated 80% response rate, we aimed to invite at least 480 students to participate in this study, with a minimum of 100 students recruited from each country. However, 642 participants were actually recruited to compensate for any incomplete or missing data.

This study targeted students from medicine and health sciences colleges (nursing, physiotherapy, nutritional sciences) that were in their second year of study or beyond. Students from both sexes and all nationalities were invited to participate. Following arrangements with education leaders and collaborators at the selected universities, students were sent a link to an online invitation letter, consent form, and questionnaire, which was circulated via the university portals.

Ethical approval

This research was approved by the University of Sharjah Research Ethics Committee (Reference number: REC-23-09-21-01-F). Participants were assured of the confidentiality and anonymity of their data, and informed that their participation was voluntary. An information sheet was available to participants at the beginning of the questionnaire that described the purpose of the study, protocol, time needed to complete the questionnaire (about 5–10 min), and the voluntary nature of participation. Those that agreed to participate selected “Agree” on the electronic consent form, and then could proceed to complete the questionnaire. Participants’ anonymity was guaranteed during the data collection process.

Data collection tool

A self-developed validated online questionnaire that assessed students’ knowledge, attitudes, and perceived barriers related to the integration of AI in HPE was used in this study. The questionnaire was based on a review of similar studies [2, 6, 20, 22,23,24,25] and comprised three main parts: sociodemographic information (age, gender, nationality, residency, marital status, specialty, educational level), prior basic knowledge and training in AI (six items), and attitudes toward AI across five different domains (26 items), including integration of AI in HPE. The first domain covered participants’ perception of the benefits of AI for healthcare systems and disease management (10 items). The second explored attitudes toward the negative impact of AI on patients’ trust in healthcare (five items). The third domain covered the perceived negative impact of AI on the future of healthcare professionals (four items). The fourth domain comprised four questions exploring participants’ perceptions of the integration of AI in HPE curricula, and the fifth domain had three items focused on perceived challenges associated with integrating AI in HPE.

Following drafting of the initial version, the questionnaire underwent evaluation of face and content validity to assess its appropriateness, logical flow, consistency, acceptance, and completion time. The questionnaire was reviewed by researchers and faculty members from the principal investigator’s institution to ensure the items captured perceived basic knowledge, attitudes, and barriers to integrating AI in HPE. Revisions were made based on feedback from this review as necessary. The questionnaire was then pilot-tested with a sample of 30 students to ensure that it was well formulated and understandable before the start of data collection via an electronic version of the questionnaire.

Each of the knowledge scale items was coded as 0 for “no” and 1 for “yes.” The total knowledge score was derived by summing the values of all six items and ranged from 0 to 6; higher scores indicated a higher level of basic knowledge about AI. Example of knowledge domain items included “Have you ever heard about AI concepts in general? And “Do you know the difference between deep learning and machine concepts?” For knowledge scale, participants were divided into three groups based on their total knowledge scores, with a maximum possible score of 6. The cutoff points were set at 60% and 80% of the total score. Participants with scores ranging from 0 to 3.6 were classified as having low knowledge, those with scores from 3.7 to 4.8 were classified as having moderate knowledge, and participants with scores above 4.8 were classified as having high knowledge.

Responses to the attitude items (all domains) were on a Likert-type scale from 0 (“strongly disagree”) to 4 (“strongly agree”) and totaling 26 items. Attitude 1 measures the perceived benefits of AI for healthcare systems and disease management and consists of 10 items while attitude 2 domain measures the perceived negative impact of AI on patients’ trust and consists of 5 items. Attitude domain 3 measures the perceived negative impact of AI on the future of healthcare professionals and consists of 4 items while attitude domain 4 measures the perceptions of the integration of AI in health profession education curricula and consist of 4 items. The last attitude domain, number 5, measures the perceived challenges related to the integration of AI in health profession education and consists of 3 items. Reverse scoring was applied to items where a “strongly agree” response reflected a negative attitude. A higher score represented a more positive attitude. The total scores for each domain were divided into three groups representing different attitude levels: low (scores < 60%), moderate (scores between 60% and 80%) and high (scores > 80%) of the total score. For example, for attitude scale number 1 with a total score of 40, participants scored less than 24 (scores < 60% of the total score) categorized as low attitude, those between 24 and 32 categorized as moderate attitudes (scores between 60% and 80%) while those above 32 (scores > 80%) categorized as high attitude.

Statistical analyses

Data analyses were performed using SPSS version 25. Categorical variables were reported as frequencies and percentages, and continuous variables as mean ± standard deviation. The findings were also summarized by country. Chi-square tests were employed to explore differences between categorical variables and countries, with one-way analysis of variance (ANOVA) used for continuous variables (i.e., the total scale scores and domain scores). A reliability analysis was conducted for the scales and domains used, with the internal consistency established using Cronbach’s alpha coefficient.

Results

Pilot testing and preliminary psychometric analysis

The combined results of the expert review and pilot testing confirmed the clarity, accuracy, acceptability, and cultural relevance of the questionnaire items. The feedback received indicated that the items were clear and free of ambiguity, and could capture students’ knowledge, attitudes, and perceived barriers to integration of AI in HPE. The overall internal consistency reliability was acceptable (Cronbach’s alphas of 0.78 and 0.81 for the prior basic knowledge and attitudes scales, respectively). The internal consistency of the five attitude domains ranged from 0.89 to 0.60. The highest reliability was reported for Domain 1 (perceived benefits of AI for the healthcare system; α = 0.89) followed by Domain 4 (perceived level of integrating AI in HPE; α = 0.83). The lowest internal consistency was noted for Domain 2 (perceived negative impact of AI on patient trust in healthcare systems; α = 0.60).

Sample characteristics

Table 1 illustrates the distribution of the sample across various sociodemographic variables. The UAE contributed the largest number of participants (n = 227, 35.4%), followed by Egypt, Jordan, and the KSA. The proportion of female participants exceeded that of males in all four countries, with a total of 474 females (73.8%) compared with 168 males (26.2%). The sample included more students aged 18–20 years (n = 364, 56.7%) compared with those aged ≥ 21 years (n = 278, 43.3%). The distribution of participants across specialties varied, with nursing students comprising the largest group (n = 218, 34%), followed by medicine (n = 173, 26.9%), nutrition (n = 129, 20.1%), and physiotherapy (n = 122, 19%). The majority of participating students (45.5%) were in their second year of study, 34.3% were in their third year, and 20.2% were in their fourth year or beyond.

Table 1 Description of participants’ sociodemographic characteristics (N = 642)
Full size table

Knowledge about AI and attitudes toward integrating AI in HPE

Prior knowledge and training and sources of AI knowledge

Table 2 outlines participants’ prior AI-related knowledge and training (six items). The total mean score for the knowledge scale was 2.93 ± 1.44. The majority (66.4%, n = 426) of participants reported a low level of prior basic AI knowledge and training, but there was a significant disparity in knowledge scores across countries (p < 0.001). The UAE had the highest percentage of participants in the low knowledge category (72.7%), followed by Egypt (66.5%), the KSA (61.5%), and Jordan (57.4%). Overall, the majority of participants had heard about AI concepts in general (76.9%), machine learning and deep learning specifically (63.1%), and had received knowledge and training in AI outside their regular formal courses (54.5%). However, 36.8% had received formal knowledge and training through their theoretical academic courses, and 19.8% had acquired knowledge and training in AI through practicum courses in their schools.

Table 2 Participants’ prior knowledge and training in artificial intelligence (AI) (N = 642)
Full size table

Significantly more participants from the UAE (n = 201, 88.5%) were familiar with the concept of AI in general compared with those from other countries (p < 0.001). Conversely, a significantly higher proportion of participants from Egypt (68.5%) were knowledgeable about machine learning or deep learning concepts compared with their counterparts from other countries (p < 0.001). Participants from Jordan demonstrated higher scores on three knowledge scale items compared with those from other countries. Specifically, they exhibited greater knowledge about the disparity between deep learning and machine learning (59.3%) and more had received formal knowledge and training in AI through theoretical (54.6%) or practicum (31.5%) courses in their schools (p < 0.001 for all). Moreover, compared with other countries, significantly more participants from Egypt (73.9%) had received knowledge and training in AI outside their courses/schools (e.g., via social media, conferences, and online training courses) (p < 0.001).

Figure 1 shows the sources of prior basic knowledge and training in AI outside of formal academic institutions. There were significant differences among countries (p < 0.001 for all). Of the 350 participants who had learned about AI from outside sources, the most prevalent source of learning across all countries was social media platforms (total sample: 66%; Egypt: 76%; UAE: 64%; Jordan: 59%; KSA: 51%), followed by educational videos/online courses (total sample: 24%; Jordan: 32%; KSA: 31%; UAE: 22%; Egypt: 19%). Conversely, the least reported sources of AI knowledge were specialized conferences/seminars/presentations (6% of all participants).

Fig. 1
figure 1

Sources of knowledge about and training in artificial intelligence (AI) by country

Full size image

Overall attitudes toward the integration of AI in HPE

Analysis of the overall attitude scores showed a significant difference across countries, with 329 participants (51.2%) demonstrating a high overall attitude. The highest percentage of participants in the high attitude group was from Egypt (59.1%), followed by the UAE (55.9%), Jordan (40.7%), and the KSA (36.5%). Only 32 participants (5%) across all countries reported low overall attitudes toward integrating AI in HPE. (See Table 3).

Table 3 Total scores and categories of attitudes toward integration of health profession education
Full size table

Attitudes toward integration of AI in HPE (by domain)

Domain 1: Perceived benefits of AI for healthcare systems and disease management

Table 4 presents the total and individual scores for items covering the perceived benefits of AI for healthcare systems and disease management, which represented the largest domain. There was no statistically significant difference among countries in the total mean score for this domain (27.84 ± 6.12, p = 0.062). Furthermore, there was no significant difference among countries by attitude category, with most (90.8%) falling into the high attitude category (p = 0.09). Five of the 10 items in this domain had the highest percentages of participants who reported agreement, with almost two-thirds agreeing/strongly agreeing with each item. For example: “AI will decrease administrative workload” (77.3%), “AI will lead to major advances in healthcare” (74.6%), “AI is generally useful in medical and health-related fields” (72.6%), “AI can be useful as a quality control tool” (71.7%), and “AI will facilitate workflow in healthcare institutions” (68.5%).

Table 4 Perceived benefits of artificial intelligence (AI) in healthcare systems and disease management (attitude domain 1–10 items) (N = 642)
Full size table

Examination of responses to individual items showed significant differences across countries. More participants from the KSA believed AI was generally useful in medical and health-related fields (87.5%), whereas more Jordanian participants believed AI would decrease the percentage of medical errors/adverse events in medical and health-related fields (68.5%). UAE participants exhibited the highest belief in the utility of AI in improving the success rate of treatment options and patient survival rates (65.2%) and its potential to decrease healthcare professionals’ administrative load (e.g. online documentation/data entry) (82.4%).

Compared with other countries a larger number of participants from Egypt agreed/strongly agreed with six of the 10 items in this domain. Specifically, they expressed belief in the ability of AI to: assist healthcare professionals in health-related decision-making processes (73.4%), facilitate workflow in healthcare institutions (73.9%), aid in the prevention and diagnosis of COVID-19 and future similar pandemics (69.5%), lead major advances in the future of medical and health-related fields (78.8%), lead healthcare delivery systems (70.9%), and serve as a quality control tool to assess the success rate of treatment options (75.4%).

Domain 2: Perceived negative impact of AI on patients’ trust in healthcare

Table 5 presents the total domain and item scores for the perceived negative impact of AI on patients’ trust. There was no statistically significant difference among countries in this domain (8.7 ± 2.81, p = 0.285). However, a significant difference was noted by category (p = 0.003), with 43.5% of participants classified in the high attitude category. Egypt had the largest proportion of participants in the high attitude category (54.7%), followed by Jordan (41.7%), the UAE (39.6%), and the KSA (31.7%).

Table 5 Perceived negative impact of artificial intelligence (AI) on patients’ trust (attitude domain 2–5 items) (N = 642)
Full size table

Examination of the scores for individual items showed that 54.2% of participants agreed/strongly agreed AI would allow patients to have control over their health, whereas 51.2% agreed/strongly agreed AI would reduce the humanistic aspect of the health profession. In addition, 49.8% of participants agreed/strongly agreed AI would be a threat to patient privacy and confidentiality, 45.6% agreed/strongly agreed AI would affect the relationship between healthcare professionals and patients, and 41.7% agreed/strongly agreed AI would negatively impact patient trust in medicine. Compared with other countries, more participants from the KSA agreed or strongly agreed with four of the five items in this domain (p < 0.001). These items were: AI allowed patients increased control over their health (69.2%), AI would decrease a patient’s confidence in medicine (62.5%), AI would reduce the humanistic aspect of the medical profession (57.7%), and concerns about privacy and confidentiality issues associated with the application of AI in healthcare settings (62.5%).

Domain 3: Perceived negative impact of AI on future healthcare professionals

Table 6 shows the total domain and item scores related to concerns about the negative impact of AI on healthcare professionals. There was no statistically significant difference in this domain among the four countries (7.59 ± 1.41, p = 0.122). However, when scores were categorized as low, medium, and high, there was a significant difference by category (p < 0.001), with 59.3% of participants falling into the low attitude category. The largest proportion of low attitudes were reported by Jordanian participants (76.9%), followed by participants from the KSA (64.4%), the UAE (55.9%), and Egypt (51.2%). Examination of responses for each item revealed the majority of participants agreed/strongly agreed with three items: healthcare professionals would have control over application and evaluation of AI in the medical field (64%), AI would reduce demands for human employees (59.3%), and healthcare professionals would find it burdensome to learn about AI (54.7%). Only 35.8% of participants expressed concern (i.e., agreed/strongly agreed) that AI would replace healthcare professionals. Significant differences were noted between countries for all items (p < 0.005 for all).

Table 6 Perceived negative impact of artificial intelligence (AI) on the future of healthcare professionals (attitude domain 3–4 items) (N = 642)
Full size table

Compared with other countries, more participants from KSA agreed/strongly agreed that AI would replace physicians, pharmacists, or nurses in the healthcare system (61.5%). More Jordanian participants believed that the introduction of AI would reduce the demand for human employees (71.3%) and that healthcare professionals would find it burdensome or challenging to learn about AI (71.3%) compared with participants from other countries. Finally, more UAE participants believed that physicians/healthcare providers would continue to have full control over the application and evaluation of AI in medical fields (74.4%).

Domain 4: perceptions of the integration of AI in HPE curricula

Table 7 presents the total domain and item scores for students’ perceptions of the integration of AI in HPE. Overall, there was no significant difference among participants in this domain (10.43 ± 2.91, p = 0.151). However, after categorizing the scores into three groups (low, medium, and high), there was a significant difference by category (p = 0.007), with 77.8% of participants in the high attitude category. Most (82.3%) participants from Egypt were classified in the high attitude category, followed by KSA (79.8%), Jordan (74.1%), and the UAE (74.0%). Only 43 participants (6.7%) were in the low attitude group for this domain. Approximately two-thirds of participants agreed/strongly agreed that AI concepts and applications should be an essential part of undergraduate theoretical courses (60.1%), clinical training (54.8%), and faculty/staff professional education/training (58.4%), and that educational institutions should prioritize AI in resource allocation (46.9%). With the exception of the first item, all items showed significant differences across countries. Jordan had the largest proportion of participants that agreed/strongly agreed that AI concepts and applications should be an essential part of both undergraduate curricula (theoretical course) (73.1%) and faculty professional development (71.3%), and that educational institutions should prioritize AI in resource allocation (58.3%). The proportion of participants that agreed/strongly agreed AI concepts and applications should be an essential part of undergraduate clinical training was highest among UAE participants (60.8%).

Table 7 Perceptions of the integration of artificial intelligence (AI) in health profession education curricula (attitude domain 4–4 items) (N = 642)
Full size table

Domain 5: perceived challenges in integrating AI in HPE

Table 8 presents the total domain and item scores for students’ perceptions of challenges facing the integration of AI in HPE (three items). There was no statistically significant difference in the scores for this domain (6.93 ± 1.44, p = 0.201). However, there was a significant difference across countries when the scores were categorized (p < 0.001). Overall, about one-third of participants were classified in the high attitude group (34.4%). Of those participants, the largest number was from the UAE (47.6%), followed by the KSA (33.7%), Jordan (32.4%), and Egypt (21.2%). Only 9.7% of participants agreed/strongly agreed that incorporating AI in HPE was challenging, whereas 61.1% agreed/strongly agreed that the use of AI in medical fields was exciting. In addition, 67.8% believed that healthcare institutions should enforce workplace policies to train medical and other healthcare staff in AI.

Table 8 Perceived challenges related to the integration of artificial intelligence (AI) in health profession education (attitude domain 5 − 3 items) (N = 642)
Full size table

Significant differences in this domain were observed across countries. Compared with other countries, more UAE participants believed that the incorporation of AI in HPE was challenging (15.9%), whereas more Jordan participants believed that integration of AI in HPE was exciting (73.1%). The proportion of those who believed that healthcare institutions should enforce workplace policies to train medical and other healthcare staff in AI was highest in Egypt (73.4%).

Barriers to integrating AI in HPE

Figure 2 illustrates the main perceived barriers to integrating AI in HPE by country. For the total sample, the three main barriers identified were lack of proper training by experts (53%), lack of awareness (50%), and lack of interest (41%). Participants from the UAE most frequently reported lack of proper training by experts as the main barrier (63%), whereas participants from Egypt identified lack of awareness about AI and lack of interest as the main barriers (57% and 50%, respectively). Furthermore, the highest proportions of responses regarding the remaining barriers were from the UAE, including resistance to change by staff (52%), lack of time (41%), lack of resources (29%), lack of support by management (28%), and lack of technical expertise (24%).

Fig. 2
figure 2

Main barriers to the integration of artificial intelligence in health profession education by country (N=642)

Full size image

Discussion

This study explored the integration of AI in HPE in four Arab countries in the Middle East region, which is an important topic of exploration. This study was particularly relevant because the results offer scientific evidence concerning integrating AI in HPE in this region. Our results can be used to leverage AI technological integration in HPE that may lead to personalized-adaptive learning and foster innovation in HPE in the Middle East. Importantly, the results can also be used to shape educational policies that are geared toward preparing competent graduates who are ready to navigate the rapid advances in the healthcare industry given the accelerated implementation of AI technology.

The findings from this study revealed that HPE students in the four examined countries had low levels of prior basic AI knowledge and had received little practical training in their educational curricula. Our results were consistent with previous studies that demonstrated a similar low literacy level in AI among HPE students. For example, a cross-sectional comparative study of Arab medical students (N = 4492) from nine countries (Libya, Egypt, Iraq, Jordan, Syria, Sudan, Algeria, Palestine, and Yemen) showed that 92.4% of students had not received formal AI knowledge or training and 87.1% had low knowledge in basic AI concepts, including machine learning and deep learning [6]. Another cross-sectional study involving 483 Jordanian HPE students (medicine, pharmacy, and dentistry) found that 50% of participants reported a lack of basic knowledge of AI; 62.6% had no idea about machine learning, 69.7% had not had any online or offline classes in AI, and 74.5% had never been taught about AI in their undergraduate studies [20].

Similar studies among medical students from different Arab countries also showed low levels of knowledge in basic AI concepts. A cross-sectional study involving medical students from Syria (N = 1,494) found only 23% had basic AI knowledge, although 45.7% showed positive attitudes toward learning about AI in medicine [19]. A multi-center study involving medical students from Lebanon (N = 365) found 45.8% were unable to define the basic AI concepts and 50.9% were unable to differentiate any of the functions/features of AI-related tools, principles of AI algorithms, or their applications in healthcare [25]. Another cross-sectional study involving medical students from the KSA (N = 1212) identified a critical gap in AI knowledge with only 26% of participants reporting a basic understanding of basic AI concepts such as deep learning and machine learning [26]. A cross-sectional study among medical students (N = 487) from Germany, Austria, and Switzerland revealed a low level of AI basic literacy, and most (94.7%) participants had not received formal training in AI during their education [27]. Interestingly, participants in all of the above studies expressed enthusiasm and positive attitudes toward integrating AI in educational curricula and readiness to learn about AI.

Our study showed there were variations among participating countries regarding the reported levels of exposure to formal theoretical and practical knowledge related to AI. Compared with other countries, more participants from Jordan had received theoretical and practical knowledge about AI concepts in their formal learning, whereas UAE participants had the lowest level of AI knowledge and training. This highlights the need to investigate the current status of AI integration in HPE programs in different countries across the region to explore opportunities for collaboration, share best practices, and create plans and protocols for the proper integration of AI in HPE. Further research is also needed to identify gaps in knowledge about integrating AI in HPE and pave the way for greater integration.

We found that a considerable number of participants (66%) had learned about AI via means such as social media rather than through formal educational courses, which may explain the low overall literacy in AI basic concepts and the variations in AI literacy across countries. Similar results were noted in a previous study [27]. Another study involving medical students (n = 121) and clinical faculty (n = 52) from Georgia showed limited AI literacy among both students and educators, with learning about AI occurring via media rather than through formal training or courses [28]. Social media can offer easy access to learning content, engagement with AI communities, networking opportunities, and current AI trends; however, the information may be fragmented, unlike when it is delivered via structured formal courses [29]. Therefore, there is an urgent need to enhance theoretical and practical knowledge about AI technology through formal, structured, and standardized courses that are delivered by experts to equip future healthcare professionals with AI competencies to meet the evolving demands of the healthcare industry [6, 18, 30].

To integrate AI in HPE, we suggest that academic institutions embed AI gradually through a collaboration between experts from engineering and medical colleges [31]. Offering AI technology through elective courses, research projects, and extra-curricular activities may facilitate acceptance and smooth integration of AI among HPE students. As the backbone of healthcare is disease diagnosis, treatment, and prevention, there is a need to integrate and adopt multidisciplinary and interprofessional collaborative learning approaches in teaching and learning to increase students’ literacy about AI applications. A recent comprehensive review of studies on AI in medical education recommended the need for interdisciplinary collaboration to unlock the full potential of AI in healthcare and equip healthcare providers with essential AI knowledge and tools for clinical practice [32]. Sharing resources and experts may facilitate greater integration of AI in HPE within and across institutions and also across the wider Arab region [6]. Revisiting educational curricula and program learning outcomes is essential to incorporate AI-related topics and ensure students are ready to embrace this technology in healthcare settings following graduation [32]. In the new structured HPE curricula, there is a need to create a proper assessment scheme that measures students’ knowledge and understanding of AI concepts and their ability to apply these to clinical training [6]. It is recommended that medical education centers across universities make it mandatory for students and educators to learn about AI technology and seek to attract international experts in the field to deliver training about AI and its application in healthcare.

Encouragingly, our participants demonstrated interest in AI and showed positive attitudes toward learning about AI and its integration in HPE. This was evident in participants’ perceptions of the benefits of AI for healthcare, such as decreasing the administrative load, facilitating workflow, improving the success rate of treatment options and patient survival, and leading to major advances in the future of medical and health-related fields. Our results were consistent with previous studies from Arab countries including Lebanon [25], , Syria [19], Jordan [20], Egypt [33], and the KSA [26]. Furthermore, studies from different parts of the world, such as Turkey [21], Australia [34], and India [17], demonstrated similar results and reported students had positive attitudes toward learning about AI technology during their education.

Our participants also expressed positive attitudes toward the integration of AI concepts in HPE curricula, which was consistent with previous studies [21, 27, 30, 35, 36]. A cross-sectional country-wide survey of Turkish medical students (N = 3018) showed that 96.2% of participants reported an urgent need to integrate AI in theory and practical training courses to improve their clinical judgment and clinical decision-making abilities [21]. Another study involving Jordanian medical students reported similar results, as participants thought that AI would revolutionize the educational system and it was necessary to learn about AI as part of training for students in medical fields at all levels [20].

Participants in this study also appeared to be relatively unconcerned about the negative impact of AI on future healthcare professionals, with only 18.3% in the high attitude group. A comparison of our results with those of previous studies indicated variation in attitudes concerning the impact of AI technology on future healthcare professionals. Unlike our study, a cross-sectional study of medical students (N = 325) from India showed that 37.6% of participants were concerned about the negative impact of AI on their future; they believed that AI would decrease the need for physicians, leading to unemployment and replacing healthcare professionals [17]. However, our results were consistent with a survey of Australian medical students (N = 134) in which only 14.8% were concerned about the impact of AI on their job security [34]. Increasing awareness about AI technology may positively impact HPE students’ perception of the future impact of AI on their job security [34].

We found that many participants (43.5%) were concerned about the negative impact of AI on patient trust in healthcare, which was consistent with findings from other similar studies. A Turkish study involving medical students (N = 3018) found students believed that using AI in medicine could devalue the medical profession (58.6%), damage patient trust (45.5%), negatively affect patient-physician relationships (42.7%), and violate professional confidentiality (44.7%) [21]. In a study from India, 52.9% of participants expressed concerns about patient trust in the healthcare system and the violation of patient confidentiality and privacy with AI technology [17]. Furthermore, 74.9% of medical students from Austria, Germany, and Switzerland reported the need for AI ethics training in medical education, which was currently inadequate [27].

These findings suggest that the ethical dimension of AI technology in healthcare should be an integral part of medical education, which may help to increase awareness of and knowledge about AI among HPE students [10, 27]. This requires collaboration among academic institutions, healthcare regulators, and bioethics experts to orient students about ethical considerations that maintain patient privacy and confidentiality and enhance patients’ trust in AI technology in the healthcare system. Cultivating ethical principles while handling big data associated with AI technology should be addressed carefully and be among the essential competencies taught by HPE programs to ensure proper use of AI technology [37]. Addressing ethical concerns involving AI technology while maintaining patient safety, privacy, and confidentiality will ensure future clinical leaders have expertise in both AI and medicine [34].

Furthermore, our participants identified barriers related to integrating AI in HPE, including lack of proper training by experts, lack of awareness, and lack of interest. These barriers were also identified in a previous integrative review in which the absence of proper training, experts, and support emerged as the main barriers to the integration of AI in HPE [10]. Similarly, a recent literature review identified technological support and a lack of experts in AI in medicine as the main barriers to the integration of AI into medical education, as reported by students from Central and Eastern European countries [38]. Lack of expertise, lack of proper training, and time constraints were the main barriers to integrating AI in HPE in similar studies from Arab countries [6, 20]. It is interesting to note that in our study, a greater number of participants from the UAE perceived barriers compared with their counterparts from other countries, as shown by their higher scores for barriers such as lack of experts and resources, lack of advanced technology, lack of time, and resistance to change. This may be attributable to limited awareness of AI technology, as UAE participants had the lowest of AI knowledge among our participants. There is a need for further research in this area, especially in the UAE, to understand factors that hinder the integration of AI in HPE. Barriers to integrating AI in HPE also need to be considered in the context of each country such as the regulatory, educational philosophy, economic, and political climates, as our study revealed variations among countries in perceptions of these barriers. Barriers could be then addressed and classified as data-, methodological-, technological-, regulatory-, policy-, and human-related factors [38]. Identifying barriers to integration of AI in HPE may support the development of unified standards and norms that leverage the integration of AI in HPE curricula to optimize the care of patients, families, and broader populations.

Limitations

The use of convenience sampling in this study might have introduced selection bias and therefore limit the generalizability of the findings to other countries and health professional groups. In addition, the creation of items to make the questionnaire general while also accommodating the unique differences between HPE specialties was challenging. Although the questionnaire underwent several validation steps to ensure its appropriateness, including extensive expert revision of items to establish face validity, piloting testing, and confirming internal consistency reliability (Cronbach’s alpha), additional validation measures might have further enhanced its psychometric properties. The instrument could also be validated in further studies involving larger populations of international students and educators, with the inclusion of other countries and other HPE programs from the Arab region. This would help to generate baseline data that allows comparisons among countries to capture a comprehensive picture of the integration of AI in HPE in this region. However, this study contributed to filling a critical gap in existing literature through investigating knowledge, attitudes, and perceived barriers related to AI integration among HPE students in this context. The inclusion of nursing, physiotherapy, and nutrition students was an additional strength because most of the existing literature only focused on the perspectives of medical students.

Conclusion

The present study demonstrates that HPE students have low levels of knowledge but positive attitudes related to integration of AI technology in HPE. This study is therefore a call for educational transformation that focuses on integrating AI as an important competency for HPE students. Barriers such as lack of expertise, lack of awareness, and lack of interest in learning about AI must be overcome to facilitate the smooth integration of basic AI concepts in HPE. Medical and other health profession educators should be cognizant of the ongoing transformation of healthcare systems and work on integrating the latest AI applications in teaching and learning to prepare competent graduates for the workforce [39]. There is also a need for an attitudinal shift among healthcare professional educators toward integrating AI in the teaching and learning process. In addition, healthcare leaders must urgently act to ensure HPE educators are positioned to contribute to the efficient and ethical deployment of AI [39]. Extensive training on how to use and interact with information systems, handling big data, engaging in information retrieval and synthesis, appraising statistics, and integrating evidence-based medicine are essential competencies for educators to enable them to integrate AI in teaching and learning. With the rapid development of technology, there is a need for policymakers, healthcare leaders, and experts in the medical field to implement regulatory standards to ensure proper integration of AI in HPE [30].

Finally, the future of integrating AI in HPE remains ambiguous and overwhelming. More research on this topic is needed as most extant studies on AI integration were cross-sectional and focused on medical students, meaning they did not capture a comprehensive picture of integrating AI in HPE. Integrating AI in the already crowded HPE curricula is challenging, and clear models and standards that guide healthcare institutions on proper and ethical AI integration in HPE are needed [18]. Studying students’ perceptions over time may also help to track changes in attitudes and guide adjustments to AI integration strategies in HPE [40]. The dynamic nature of AI in education necessitates ongoing vigilance, flexibility, and adaptability to changes in AI technology and HPE. Promoting awareness of AI tools among HPE educators and students is also critical for creating and adopting AI-based solutions in medical education.

Data availability

The datasets used and analyzed during the present study are available from the corresponding author upon reasonable request.

References

  1. Janiesch C, Zschech P, Heinrich K. Machine learning and deep learning. Electron Markets. 2021;31(3):685–95.

    Article  Google Scholar 

  2. Nagi F, Salih R, Alzubaidi M, Shah H, Alam T, Shah Z et al. Applications of artificial intelligence (AI) in medical education: a scoping review. Healthcare Transformation with Informatics and Artificial Intelligence. 2023:648 – 51.

  3. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69:S36–40.

    Article  Google Scholar 

  4. Obermeyer Z, Emanuel EJ. Predicting the future—big data, machine learning, and clinical medicine. N Engl J Med. 2016;375(13):1216.

    Article  Google Scholar 

  5. Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447–53.

    Article  Google Scholar 

  6. Allam AH, Eltewacy NK, Alabdallat YJ, Owais TA, Salman S, Ebada MA. Knowledge, attitude, and perception of arab medical students towards artificial intelligence in medicine and radiology: a multi-national cross-sectional study. Eur Radiol. 2023:1–14.

  7. Briganti G, Le Moine O. Artificial intelligence in medicine: today and tomorrow. Front Med. 2020;7:509744.

    Article  Google Scholar 

  8. Saibene A, Assale M, Giltri M. Expert systems: definitions, advantages and issues in medical field applications. Expert Syst Appl. 2021;177:114900.

    Article  Google Scholar 

  9. Çalışkan SA, Demir K, Karaca O. Artificial intelligence in medical education curriculum: an e-Delphi study for competencies. PLoS ONE. 2022;17(7):e0271872.

    Article  Google Scholar 

  10. Chan KS, Zary N. Applications and challenges of implementing artificial intelligence in medical education: integrative review. JMIR Med Educ. 2019;5(1):e13930.

    Article  Google Scholar 

  11. Pizzolla I, Aro R, Duez P, De Lièvre B, Briganti G. Integrating artificial intelligence into medical education: lessons learned from a Belgian initiative. J Interact Learn Res. 2023;34(2):401–24.

    Google Scholar 

  12. Mir MM, Mir GM, Raina NT, Mir SM, Mir SM, Miskeen E, et al. Application of artificial intelligence in medical education: current scenario and future perspectives. J Adv Med Educ Professionalism. 2023;11(3):133.

    Google Scholar 

  13. Hamdy H, Sreedharan J, Rotgans JI, Zary N, Bahous SA, Venkatramana M, et al. Virtual clinical encounter examination (VICEE): a novel approach for assessing medical students’ non-psychomotor clinical competency. Med Teach. 2021;43(10):1203–9.

    Article  Google Scholar 

  14. Masters K. Artificial intelligence in medical education. Med Teach. 2019;41(9):976–80.

    Article  Google Scholar 

  15. Zarei M, Mamaghani HE, Abbasi A, Hosseini M-S. Application of artificial intelligence in medical education: a review of benefits, challenges, and solutions. Med Clínica Práctica. 2024;7(2):100422.

    Article  Google Scholar 

  16. Paranjape K, Schinkel M, Panday RN, Car J, Nanayakkara P. Introducing artificial intelligence training in medical education. JMIR Med Educ. 2019;5(2):e16048.

    Article  Google Scholar 

  17. Jackson P, Gayathri P, Babu C, Tony C, Jack DS, Davis D et al. Artificial Intelligence in Medical Education-Perception Among Medical Students. 2024.

  18. Gordon M, Daniel M, Ajiboye A, Uraiby H, Xu NY, Bartlett R et al. A scoping review of artificial intelligence in medical education: BEME Guide No. 84. Medical Teacher. 2024:1–25.

  19. Swed S, Alibrahim H, Elkalagi NKH, Nasif MN, Rais MA, Nashwan AJ, et al. Knowledge, attitude, and practice of artificial intelligence among doctors and medical students in Syria: a cross-sectional online survey. Front Artif Intell. 2022;5:1011524.

    Article  Google Scholar 

  20. Al-Qerem W, Eberhardt J, Jarab A, Al Bawab AQ, Hammad A, Alasmari F, et al. Exploring knowledge, attitudes, and practices towards artificial intelligence among health professions’ students in Jordan. BMC Med Inf Decis Mak. 2023;23(1):288.

    Article  Google Scholar 

  21. Civaner MM, Uncu Y, Bulut F, Chalil EG, Tatli A. Artificial intelligence in medical education: a cross-sectional needs assessment. BMC Med Educ. 2022;22(1):772.

    Article  Google Scholar 

  22. Al Hadithy ZA, Al Lawati A, Al-Zadjali R, Al Sinawi H. Knowledge, attitudes, and perceptions of artificial intelligence in healthcare among medical students at Sultan Qaboos University. Cureus. 2023;15(9).

  23. Busch F, Hoffmann L, Truhn D, Ortiz-Prado E, Makowski MR, Bressem KK et al. Medical students’ perceptions towards artificial intelligence in education and practice: A multinational, multicenter cross-sectional study. medRxiv. 2023:2023.12. 09.23299744.

  24. Syed W, Basil A, Al-Rawi M. Assessment of awareness, perceptions, and opinions towards artificial intelligence among healthcare students in Riyadh, Saudi Arabia. Medicina. 2023;59(5):828.

    Article  Google Scholar 

  25. Daher OA, Dabbousi AA, Chamroukh R, Saab AY, Al Ayoubi AR, Salameh P. Artificial Intelligence: knowledge and attitude among Lebanese Medical Students. Cureus. 2024;16(1).

  26. Alghamdi SA, Alashban Y. Medical science students’ attitudes and perceptions of artificial intelligence in healthcare: a national study conducted in Saudi Arabia. J Radiation Res Appl Sci. 2024;17(1):100815.

    Google Scholar 

  27. Weidener L, Fischer M. Artificial intelligence in medicine: cross-sectional study among medical students on application, education, and ethical aspects. JMIR Med Educ. 2024;10(1):e51247.

    Article  Google Scholar 

  28. Wood EA, Ange BL, Miller DD. Are we ready to integrate artificial intelligence literacy into medical school curriculum: students and faculty survey. J Med Educ Curric Dev. 2021;8:23821205211024078.

    Article  Google Scholar 

  29. Ng DTK, Chu SKW. Motivating students to learn AI through Social networking sites: a Case Study in Hong Kong. Online Learn. 2021;25(1):195–208.

    Article  Google Scholar 

  30. Sun L, Yin C, Xu Q, Zhao W. Artificial intelligence for healthcare and medical education: a systematic review. Am J Translational Res. 2023;15(7):4820.

    Google Scholar 

  31. Imran N, Jawaid M. Artificial intelligence in medical education: are we ready for it? Pakistan J Med Sci. 2020;36(5):857.

    Google Scholar 

  32. Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ. 2023;23(1):689.

    Article  Google Scholar 

  33. Khater AS, Zaaqoq AA, Wahdan MM, Ashry S. Knowledge and attitude of Ain Shams University Medical Students towards Artificial Intelligence and its application in Medical Education and Practice. Educational Res Innov J. 2023;3(10):29–42.

    Article  Google Scholar 

  34. Stewart J, Lu J, Gahungu N, Goudie A, Fegan PG, Bennamoun M, et al. Western Australian medical students’ attitudes towards artificial intelligence in healthcare. PLoS ONE. 2023;18(8):e0290642.

    Article  Google Scholar 

  35. Doumat G, Daher D, Ghanem N-N, Khater B. Knowledge and attitudes of medical students in Lebanon toward artificial intelligence: a national survey study. Front Artif Intell. 2022;5:1015418.

    Article  Google Scholar 

  36. Wang F, Preininger A. AI in health: state of the art, challenges, and future directions. Yearb Med Inform. 2019;28(01):016–26.

    Article  Google Scholar 

  37. Khalid N, Qayyum A, Bilal M, Al-Fuqaha A, Qadir J. Privacy-preserving artificial intelligence in healthcare: techniques and applications. Comput Biol Med. 2023:106848.

  38. Tachkov K, Zemplenyi A, Kamusheva M, Dimitrova M, Siirtola P, Pontén J, et al. Barriers to use artificial intelligence methodologies in health technology assessment in central and east European countries. Front Public Health. 2022;10:921226.

    Article  Google Scholar 

  39. Lomis K, Jeffries P, Palatta A, Sage M, Sheikh J, Sheperis C et al. Artificial intelligence for health professions educators. NAM perspectives. 2021;2021.

  40. Algabri HK, Kharade K, Kamat R. Promise, threats, and personalization in higher education with artificial intelligence. Webology. 2021;18(6).

Download references

Acknowledgements

We would like to acknowledge the participating institutions for their contribution to this study and the healthcare leaders who granted access to students in the selected universities.

Funding

Not applicable.

Author information

Author notes

  1. Ali Shorbagi

    Present address: College of Medicine, Clinical Sciences Department , University of Sharjah, Sharjah, UAE

  2. Alham Al-Sharman

    Present address: Department of Physiotherapy, College of Health Sciences, University of Sharjah, Sharjah, UAE

  3. Hadia Radwan

    Present address: Critical Health Nursing, College of Nursing, Riyadh Elm University, Riyadh, Saudi Arabia

  4. Heba Hijazi

    Present address: Department of Health Care Management, College of Health Sciences, University of Sharjah, Sharjah, UAE

  5. All authors contributed equally to this work and share primary authorship for the paper.

Authors and Affiliations

  1. Department of Nursing, College of Health Sciences, University of Sharjah, Sharjah, UAE

    Wegdan Bani Issa, Fatma Refaat Ahmed, Nabeel Al-Yateem & Richard Mottershead

  2. Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, UAE

    Wegdan Bani Issa & Dana N. Abdelrahim

  3. Department of Rehabilitation Sciences, Faculty of Applied Medical Sciences, University of Science and Technology, Irbid, Jordan

    Alham Al-Sharman

  4. Adult Health Nursing Department, Faculty of Nursing/WHO Collaborating Center, Jordan University of Science and Technology, Irbid, Jordan

    Mohammad Rababa

  5. Critical Health Nursing, College of Nursing, Riyadh Elm University, Riyadh, Saudi Arabia

    Khalid Al-Majeed

  6. Department of Clinical Nutrition and Dietetics, College of Health Sciences, University of Sharjah, Sharjah, UAE

    Hadia Radwan & Nada Abbas

  7. Department of Health Management and Policy, Faculty of Medicine, Jordan University of Science and Technology, P.O. Box 3030, Irbid, 22110, Jordan

    Heba Hijazi

  8. California State University, Dominguez Hills, San Diego, CA, USA

    Wafa Khasawneh

  9. Department of Entrepreneurship, Innovation and Marketing, United Arab Emirates University, Al Ain, UAE

    Ibrahim Ali

  10. Institute for Social Research, Survey Research Center, University of Michigan, Ann Arbor, USA

    Randa Fakhry

Authors
  1. Wegdan Bani Issa
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Ali Shorbagi
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Alham Al-Sharman
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Mohammad Rababa
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Khalid Al-Majeed
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Hadia Radwan
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Fatma Refaat Ahmed
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Nabeel Al-Yateem
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Richard Mottershead
    View author publications

    You can also search for this author inPubMed Google Scholar

  10. Dana N. Abdelrahim
    View author publications

    You can also search for this author inPubMed Google Scholar

  11. Heba Hijazi
    View author publications

    You can also search for this author inPubMed Google Scholar

  12. Wafa Khasawneh
    View author publications

    You can also search for this author inPubMed Google Scholar

  13. Ibrahim Ali
    View author publications

    You can also search for this author inPubMed Google Scholar

  14. Nada Abbas
    View author publications

    You can also search for this author inPubMed Google Scholar

  15. Randa Fakhry
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

All authors contributed to project conception, study design, data interpretation and analysis, and drafting the manuscript. WBI, AS, AA, MR, KM, HR, HH, FR, NY, RAF, NA, and DNA contributed to the study design. WBI, NA, and DNA contributed to data analysis and interpretation. AA contributed to study design and data acquisition, analysis, and interpretation. WBI, RAF, AI, HH, DNA, and WK contributed to project conception, study design, and data interpretation and analysis. WBI, AS, HR, RF, AI, WK, HH, WK, and DNA revised multiple manuscript drafts, including critical revision for important intellectual content. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Wegdan Bani Issa.

Ethics declarations

Ethics approval and consent to participate

Research ethics approval was obtained from the Research Ethics Committee, University of Sharjah, United Arab Emirates (reference number REC-23-09-21-01-F). All study procedures were conducted in accordance with relevant guidelines and regulations. Informed consent was obtained from all participants.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Issa, W.B., Shorbagi, A., Al-Sharman, A. et al. Shaping the future: perspectives on the Integration of Artificial Intelligence in health profession education: a multi-country survey. BMC Med Educ 24, 1166 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12909-024-06076-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12909-024-06076-9

Keywords

BMC Medical Education

ISSN: 1472-6920

Contact us