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Cited by 78Artificial Intelligence in Healthcare and EducationInnovations in Medical EducationArtificial Intelligence in Education

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

Open access
Issa, Wegdan Bani, Shorbagi, Ali, Al-Sharman, Alham, Rababa, Mohammad, Al-Majeed, Khalid, Radwan, Hadia, Refaat Ahmed, Fatma, Al-Yateem, Nabeel +7 more·2024·BMC Medical Education
DOI 10.1186/s12909-024-06076-9PMC PMC11488068

Published version of a preprint

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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.

AI snapshot

Written from the paper’s full text.

TL;DR

This cross-sectional multi-country survey of 642 health profession students in Jordan, KSA, UAE, and Egypt found low AI knowledge but generally positive attitudes toward integrating AI in health profession education. Country variations were notable, and the main perceived barriers were lack of expert training, awareness, and interest.

Key findings

  • 66.4% reported low prior basic AI knowledge and training; the UAE had the largest proportion with low knowledge (72.7%). The total knowledge score was 2.93 ± 1.44, and 54.5% had learned about AI outside their courses, mainly through social media (66%).
  • 51.2% had high overall attitudes toward AI integration; 77.6% supported integrating AI in HPE, especially in Egypt (82.3%). Most participants viewed AI in healthcare positively (91%).
  • 43.5% were in the high attitude category for perceived negative impact of AI on patient trust, particularly in Egypt (54.7%); only 18.1% were concerned about AI’s impact on future healthcare professionals, with the largest proportion from Egypt (33.0%).
  • 34.4% perceived AI integration as challenging, notably in the UAE (47.6%). Common barriers were lack of expert training (53%), lack of awareness (50%), and lack of interest (41%).

Methods

  • Cross-sectional online survey conducted February–October 2023 using non-probability convenience sampling of 642 medical, nursing, physiotherapy, and clinical nutrition students from four public universities in Jordan, KSA, UAE, and Egypt; participants were in their second year or beyond.
  • A self-developed validated questionnaire covered sociodemographics, prior AI knowledge/training (6 items), and attitudes across five domains (26 items). Knowledge was scored 0–6 and categorized as low, moderate, or high; attitude items used a 0–4 Likert scale with reverse scoring and were categorized similarly.
  • Analyses used SPSS version 25, with frequencies/percentages, means ± SD, chi-square tests, one-way ANOVA, and Cronbach’s alpha for reliability.

Limitations

  • The paper states that convenience sampling may have introduced selection bias and limits generalizability to other countries and health professional groups.
  • The paper states that creating questionnaire items general enough while accommodating differences between HPE specialties was challenging, and that additional validation measures might have further enhanced psychometric properties.
  • Evident from the text: the cross-sectional design cannot track changes in attitudes over time, and the uneven country/specialty distributions (e.g., UAE n = 227 vs. KSA n = 104; nursing 34%, medicine 26.9%) may affect cross-country comparisons.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1186/s12909-024-06076-9

Cites

39 of 39 references verified0 citation errors · 0 not checkable

Statistics

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Claims the paper makes

0 of 67 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    A perceived negative impact of AI on patient trust was expressed by 43.5% of participants.

    “A perceived negative impact of AI on patient trust was expressed by 43.5% of participants, particularly in Egypt (54.7%).”
    Quote in the paperNot a substantial claim
  • Findingown data

    Overall, 76.9% of participants had heard about AI concepts in general.

    “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%).”
    Quote in the paperNot a substantial claim
  • Findingown data

    Educational videos/online courses were the next most reported source of AI knowledge (total sample: 24%).

    “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%).”
    Quote in the paperNot a substantial claim
  • Findingown data

    63.1% of participants had heard about machine learning and deep learning specifically.

    “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%).”
    Quote in the paperNot a substantial claim
  • Findingown data

    More Jordanian participants believed AI would decrease the percentage of medical errors/adverse events (68.5%).

    “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%).”
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    Findingown data

    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%).

    “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%).”
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    Findingown data

    Analysis of overall attitude scores showed a significant difference across countries, with 329 participants (51.2%) demonstrating a high overall attitude.

    “Analysis of the overall attitude scores showed a significant difference across countries, with 329 participants (51.2%) demonstrating a high overall attitude.”
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    Findingown data

    In Domain 1, the five items with highest agreement were 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%).

    “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%).”
    Quote in the paperNot a substantial claim

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    Findingown data

    Participants from Jordan demonstrated higher scores on three knowledge scale items compared with those from other countries: disparity between deep learning and machine learning (59.3%), formal theoretical training (54.6%), and practicum training (31.5%) (p < 0.001 for all).

    “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).”
    Quote in the paperNot a substantial claim

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    Findingown data

    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%).

    “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%).”
    Quote in the paperNot a substantial claim

  • The quote only partly states this claim.

    Findingown data

    Egypt had the largest proportion expressing perceived negative impact of AI on patient trust (54.7%).

    “A perceived negative impact of AI on patient trust was expressed by 43.5% of participants, particularly in Egypt (54.7%).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    There was no significant difference among countries by attitude category in Domain 1, with most (90.8%) falling into the high attitude category (p = 0.09).

    “Furthermore, there was no significant difference among countries by attitude category, with most (90.8%) falling into the high attitude category (p = 0.09).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Social media was the most prevalent source of AI knowledge in Egypt (76%), the UAE (64%), Jordan (59%), and the KSA (51%).

    “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%).”
    Quote in the paperNot a substantial claim

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    Findingown data

    A significant difference was noted by category in Domain 2 (p = 0.003), with 43.5% of participants classified in the high attitude category.

    “However, a significant difference was noted by category (p = 0.003), with 43.5% of participants classified in the high attitude category.”
    Quote in the paperNot a substantial claim

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    Findingown data

    Egypt had the largest proportion of participants in the high attitude category in Domain 2 (54.7%), followed by Jordan (41.7%), the UAE (39.6%), and the KSA (31.7%).

    “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%).”
    Quote in the paperNot a substantial claim

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    Findingown data

    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.

    “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.”
    Quote in the paperSubstantial claim

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    Findingown data

    Jordanian participants had the largest proportion of low attitudes (76.9%) in the perceived negative impact domain, followed by KSA (64.4%), UAE (55.9%), and Egypt (51.2%).

    “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%).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Of those in the high attitude group for perceived challenges, the largest number was from UAE (47.6%), followed by KSA (33.7%), Jordan (32.4%), and Egypt (21.2%).

    “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%).”
    Quote in the paperNot a substantial claim

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    Findingown data

    Most (82.3%) participants from Egypt were classified in the high attitude category for perceptions of integration of AI in HPE curricula, followed by KSA (79.8%), Jordan (74.1%), and UAE (74.0%).

    “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%).”
    Quote in the paperNot a substantial claim

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    Findingown data

    Overall, about one-third of participants were classified in the high attitude group for perceived challenges in integrating AI in HPE (34.4%).

    “Overall, about one-third of participants were classified in the high attitude group (34.4%).”
    Quote in the paperNot a substantial claim

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    Findingown data

    Jordan had the largest proportion of participants that agreed/strongly agreed that educational institutions should prioritize AI in resource allocation (58.3%).

    “and that educational institutions should prioritize AI in resource allocation (58.3%).”
    Quote in the paperNot a substantial claim

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    Findingown data

    Participants perceived 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.

    “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.”
    Quote in the paperSubstantial claim

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    Findingown data

    Our participants demonstrated interest in AI and showed positive attitudes toward learning about AI and its integration in HPE.

    “Encouragingly, our participants demonstrated interest in AI and showed positive attitudes toward learning about AI and its integration in HPE.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingnone given

    The present study demonstrates that HPE students have low levels of knowledge but positive attitudes related to integration of AI technology in HPE.

    “The present study demonstrates that HPE students have low levels of knowledge but positive attitudes related to integration of AI technology in HPE.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    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.

    “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.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Our participants identified barriers related to integrating AI in HPE, including lack of proper training by experts, lack of awareness, and lack of interest.

    “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.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    The questionnaire was pilot-tested with a sample of 30 students.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    The questionnaire underwent evaluation of face and content validity.

    “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.”
    Quote in the paperNot a substantial claim

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    Premisecitation

    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.

    “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].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [10] Chan KS, Zary N. Applications and challenges of implementing artificial intelligence in medical education: integrative review. JMIR Med Educ. 2019;5(1):e13930. source

      Not checked in the free run.

    • cites [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.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Students reported AI technology aided them in receiving specialized assistance from educators and identifying learning needs and knowledge gaps.

    “Similarly, students reported AI technology aided them in receiving specialized assistance from educators and identifying learning needs and knowledge gaps [11].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductown data

    The study aimed to invite at least 480 students, with a minimum of 100 students recruited from each country, but 642 participants were actually recruited to compensate for any incomplete or missing data.

    “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.”
    Quote in the paperNot a substantial claim

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    Premisecitation

    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.

    “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 [14–16].”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [14] Masters K. Artificial intelligence in medical education. Med Teach. 2019;41(9):976–80. source

      Not checked in the free run.

    • cites [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. source

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    • cites [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. source

      Not checked in the free run.

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    Study design and conductown data

    The highest reliability was reported for Domain 1 (α = 0.89) followed by Domain 4 (α = 0.83).

    “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).”
    Quote in the paperNot a substantial claim

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    Premisecitation

    Training in AI concepts must be integrated into undergraduate HPE curricula to ensure students are exposed to AI early so they can navigate its application during practice.

    “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].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    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.

    “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].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [10] Chan KS, Zary N. Applications and challenges of implementing artificial intelligence in medical education: integrative review. JMIR Med Educ. 2019;5(1):e13930. source

      Not checked in the free run.

    • cites [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.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    AI is widely recognized in healthcare as a ground-breaking technological innovation that leads to better treatment outcomes.

    “Similar to other fields and industries, AI is widely recognized in healthcare as a ground-breaking technological innovation that leads to better treatment outcomes [2–5].”
    Quote in the paperNot a substantial claim0 of 4 sources checked
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  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

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    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.

    “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 [7–9].”
    Quote in the paperNot a substantial claim0 of 3 sources checked
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    In the same Jordanian study, 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.

    “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].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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. source

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    Premisecitation

    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.

    “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].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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. source

      Not checked in the free run.

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    Premisecitation

    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.

    “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].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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. source

      Not checked in the free run.

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    Premisecitation

    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.

    “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].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    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.

    “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].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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. source

      Not checked in the free run.

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    Participants in all of the above studies expressed enthusiasm and positive attitudes toward integrating AI in educational curricula and readiness to learn about AI.

    “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.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [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. source

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    • cites [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. source

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    • cites [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. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    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%).

    “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].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Similar results to the social media learning finding were noted in a previous study.

    “Similar results were noted in a previous study [27].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    The high proportion of learning about AI via social media may explain the low overall literacy in AI basic concepts and the variations in AI literacy across countries.

    “which may explain the low overall literacy in AI basic concepts and the variations in AI literacy across countries.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Studies from different parts of the world, such as Turkey, Australia, and India, demonstrated similar results and reported students had positive attitudes toward learning about AI technology during their education.

    “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.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [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. source

      Not checked in the free run.

    • cites [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. source

      Not checked in the free run.

    • cites [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. source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    74.9% of medical students from Austria, Germany, and Switzerland reported the need for AI ethics training in medical education, which was currently inadequate.

    “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].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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. source

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    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.

    “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].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [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. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    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.

    “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].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    The higher perceived barriers among UAE participants may be attributable to limited awareness of AI technology, as UAE participants had the lowest AI knowledge among our participants.

    “This may be attributable to limited awareness of AI technology, as UAE participants had the lowest of AI knowledge among our participants.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Study design and conductnone given

    Additional validation measures might have further enhanced the questionnaire's psychometric properties.

    “additional validation measures might have further enhanced its psychometric properties.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Study design and conductnone given

    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.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Premisecitation

    Integrating AI in the already crowded HPE curricula is challenging.

    “Integrating AI in the already crowded HPE curricula is challenging”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationown data

    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.

    “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.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    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.

    “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].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [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. source

      Not checked in the free run.

    • cites [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. source

      Not checked in the free run.

  • May combine two claims.

    Study design and conductreasoning

    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.

    “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.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductnone given

    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).

    “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)”
    Quote in the paperNot a substantial claim
53 of 53

  • The paper relies on this without support.

    Participants' self-reported knowledge and attitudes accurately reflect their actual knowledge and attitudes.

    “Data were collected using a self-administered, web-based survey.”

  • The paper relies on this without support.

    The selected public universities and convenience sample are representative enough of HPE students in the four Arab countries for the findings to inform collective curricula development.

    “The researchers selected one large public university from each country that had medicine and other health-related programs.”

  • The paper relies on this without support.

    The survey instrument measures the same constructs equivalently across the four countries.

    “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 paper relies on this without support.

    AI technology is being rapidly implemented in healthcare and will continue to transform the healthcare industry.

    “given the accelerated implementation of AI technology”
4 of 4

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Domain 1 high-score count is incompatible with its mean

    Major

    On a 0-40 scale, a high score (>80%) must be at least 33 and a moderate score at least 24. The reported 583 high and 41 moderate scores require a mean of at least 31.5, not 27.84.

  • Domain 2 score categories are incompatible with its mean

    Major

    On a 0-20 scale, 279 high scores must be at least 17 and 96 moderate scores at least 12. Even if every other score were zero, the mean would be at least 9.18, exceeding the reported 8.7.

  • Domain 5 score categories are incompatible with its mean

    Major

    On a 0-12 scale, 221 high scores must be at least 10 and 383 moderate scores at least 8. Their minimum possible contribution gives a mean of 8.21, above the reported 6.93.

  • Domain 4 score categories are incompatible with its mean

    Major

    On a 0-16 scale, the 498 high scores must be at least 13 and the 101 moderate scores at least 10. Those scores alone require a mean of at least 11.66, rather than 10.43.

  • Machine-learning knowledge has two reported p-values

    Minor

    The narrative reports p < 0.001 for the country comparison of familiarity with machine-learning or deep-learning concepts, whereas the corresponding Table 2 row reports p = 0.002.

  • Social-media percentage is applied to the wrong group

    Minor

    The 66% social-media figure is stated for the 350 participants who learned about AI outside their courses, not for all 642 participants as the discussion says.

6 of 6

Cites

Flagged first, then by citations

Research works printed but not linked: 2

  1. 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.
  2. 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.

Cited by

Sorted by citations

Related

Authors
Issa, W. B., Shorbagi, A., Al-Sharman, A., Rababa, M., Al-Majeed, K., Radwan, H., Refaat Ahmed, F., Al-Yateem, N., Mottershead, R., Abdelrahim, D. N., Hijazi, H., Khasawneh, W., Ali, I., Abbas, N., & Fakhry, R.
Affiliations
  • University of Sharjah (AE)
  • Jordan University of Science and Technology (JO)
  • Riyadh Elm University (SA)
  • California State University, Dominguez Hills (US)
  • United Arab Emirates University (AE)
  • University of Michigan (US)
Corresponding author
Issa, Wegdan Bani
ORCID
Venue
BMC Medical Education
Publisher
Springer Science and Business Media LLC
Published
2024-10-18
Volume
24
Issue
1
Pages
1166-1166
ISSN
1472-6920 (ISSN-L)
DOI
10.1186/s12909-024-06076-9
PMC
PMC11488068
OpenAlex
W4403548675
Semantic Scholar
baa05dcce56ee10c618f9ad848d63e700ce64194
Cited by
78
References
39
Citations by year
2026: 43 · 2025: 33 · 2024: 2
FWCI
7.29
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Medical Education (publishedVersion, cc-by-nc-nd)
  • Research Square (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
Keywords
artificial intelligence in healthcare, AI integration in curriculum, AI adoption barriers, patient trust, medical students, nursing students, physiotherapy students, personalized learning technologies
MeSH terms
Adult, Artificial Intelligence, Attitude of Health Personnel, Cross-Sectional Studies, Curriculum, Egypt, Female, Health Occupations, Humans, Jordan, Health Knowledge, Attitudes, Practice, Male, Surveys and Questionnaires, Saudi Arabia, Students, Health Occupations, United Arab Emirates, Young Adult

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-07.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-07.

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