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Cited by 74Artificial Intelligence in Healthcare and EducationInnovations in Medical EducationArtificial Intelligence in Education

Perceptions of undergraduate medical students on artificial intelligence in medicine: mixed-methods survey study from Palestine

Open access
Jebreen, Kamel, Radwan, Eqbal, Kammoun-Rebai, Wafa, Alattar, Etimad, Radwan, Afnan, Safi, Walaa, Radwan, Walaa, Alajez, Mohammed·2024·BMC Medical Education
DOI 10.1186/s12909-024-05465-4PMC PMC11077786
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Abstract

Background: The current applications of artificial intelligence (AI) in medicine continue to attract the attention of medical students. This study aimed to identify undergraduate medical students’ attitudes toward AI in medicine, explore present AI-related training opportunities, investigate the need for AI inclusion in medical curricula, and determine preferred methods for teaching AI curricula. Methods: This study uses a mixed-method cross-sectional design, including a quantitative study and a qualitative study, targeting Palestinian undergraduate medical students in the academic year 2022–2023. In the quantitative part, we recruited a convenience sample of undergraduate medical students from universities in Palestine from June 15, 2022, to May 30, 2023. We collected data by using an online, well-structured, and self-administered questionnaire with 49 items. In the qualitative part, 15 undergraduate medical students were interviewed by trained researchers. Descriptive statistics and an inductive content analysis approach were used to analyze quantitative and qualitative data, respectively. Results: From a total of 371 invitations sent, 362 responses were received (response rate = 97.5%), and 349 were included in the analysis. The mean age of participants was 20.38 ± 1.97, with 40.11% (140) in their second year of medical school. Most participants (268, 76.79%) did not receive formal education on AI before or during medical study. About two-thirds of students strongly agreed or agreed that AI would become common in the future (67.9%, 237) and would revolutionize medical fields (68.7%, 240). Participants stated that they had not previously acquired training in the use of AI in medicine during formal medical education (260, 74.5%), confirming a dire need to include AI training in medical curricula (247, 70.8%). Most participants (264, 75.7%) think that learning opportunities for AI in medicine have not been adequate; therefore, it is very important to study more about employing AI in medicine (228, 65.3%). Male students (3.15 ± 0.87) had higher perception scores than female students (2.81 ± 0.86) (p < 0.001). The main themes that resulted from the qualitative analysis of the interview questions were an absence of AI learning opportunities, the necessity of including AI in medical curricula, optimism towards the future of AI in medicine, and expected challenges related to AI in medical fields. Conclusion: Medical students lack access to educational opportunities for AI in medicine; therefore, AI should be included in formal medical curricula in Palestine.

AI snapshot

Written from the paper’s full text.

TL;DR

This mixed-methods survey of Palestinian undergraduate medical students found limited formal AI education and strong demand for AI inclusion in medical curricula. Most students were optimistic about AI’s future in medicine, but male students had higher perception scores regarding AI in medical education than female students.

Key findings

  • Among 349 analyzed respondents (response rate 97.5%), 268 (76.79%) had not received formal education about AI before or during medical study, and 260 (74.5%) reported no prior training in AI use in medicine during formal medical education.
  • Most participants thought AI would become common in the future (237, 67.9%) and revolutionize medical fields (240, 68.7%); 247 (70.8%) said AI should be specifically included in the medical curriculum, and 264 (75.7%) thought learning opportunities for AI in medicine were inadequate.
  • Male students had higher perception scores than female students for AI in medical education (3.15 ± 0.87 vs 2.81 ± 0.86, p < 0.001); no significant differences were found by age, year of study, background in mathematics/statistics/computer science, technology experience, family AI degree, or university.
  • Qualitative interviews produced four main themes: absence of current AI learning opportunities, necessity of including AI learning in medical curricula, optimism towards the future of AI in medicine, and expected challenges related to AI in medical fields.

Methods

  • Mixed-methods cross-sectional study conducted in the West Bank and Gaza Strip, Palestine, from June 15, 2022, to May 30, 2023, targeting undergraduate medical students in the 2022–2023 academic year.
  • Quantitative phase used a 49-item online self-administered questionnaire; 371 invitations were sent, 362 responses received, and 349 included in analysis. Qualitative phase used 15 individual interviews conducted via Google Meet for 5–20 minutes.
  • Quantitative data were analyzed with descriptive statistics and Mann-Whitney U or Kruskal-Wallis H tests in R-Cran version 4.3.0; qualitative data were analyzed using inductive content analysis.

Limitations

  • Stated: the sample was non-representative and mainly female (71.92%), limiting generalizability; the study covered only seven Palestinian universities with medical faculties; self-selection bias likely meant students interested in AI and medical technologies were more likely to participate; most participants were from the West Bank; and the study lacked sensitivity analyses.
  • Evident from the text: the quantitative sample used nonprobability convenience sampling, and the study design was cross-sectional, so causal or longitudinal inferences cannot be drawn.
  • Evident from the text: the qualitative interviews were brief (5–20 minutes) and relied on note-taking during interviews, which may limit depth and detail of qualitative findings.

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

Integrity signals

Registry

Crossref · 10.1186/s12909-024-05465-4

Cites

68 of 68 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 13 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    The study found no significant difference in median scores of participants' perception of AI in medical education based on age, year of medical study, having a background in mathematics, statistics, or computer science, having a good experience in technology, having a parent or sibling with an AI major, and type of university.

    “The study found no significant difference in the median scores of participants’ perception of AI in medical education based on age, year of medical study, having a background in mathematics, statistics, or computer science, having a good experience in technology, having a parent or sibling with an AI major, and type of university.”
    Quote in the paperNot a substantial claim
  • Findingown data

    There are no universities in Palestine that have a formal AI program included in the annual plan.

    “Our study revealed that there are no universities that have a formal AI program included in the annual plan.”
    Quote in the paperSubstantial claim
  • Findingown data

    The overall median score of Likert responses for all participants regarding understanding of AI in medical education was 3.00 (IQR = 1).

    “The overall median score of Likert responses for all participants was 3.00 (IQR = 1).”
    Quote in the paperNot a substantial claim
  • Findingown data

    68.7% (240) of participants strongly agreed or agreed that AI would revolutionize medical fields.

    “revolutionize medical fields (68.7%, 240)”
    Quote in the paperNot a substantial claim
  • Findingown data

    A high percentage of interviewed participants confirmed that the current learning opportunities regarding AI were informal and outside their medical curricula.

    “A high percentage of interviewed participants confirmed that the current learning opportunities regarding AI were informal and outside their medical curricula.”
    Quote in the paperNot a substantial claim
  • May combine two claims. 23
  • Classified as probably not a substantial claim. 21
  • The quote only partly states this claim. 1

  • May combine two claims.

    Findingown data

    Social media platforms (119, 34.1%), medical training (47, 13.47%), elective courses (46, 13.18%), and research projects (45, 12.89%) were the most common sources through which students heard about the uses and applications of AI in medicine.

    “Social media platforms (119, 34.1%), medical training (47, 13.47%), elective courses provided by the university (46, 13.18%), as well as research projects with research medical teams (45, 12.89%), were the most common sources through which students heard about the uses and applications of AI in medicine (Table 4).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    There was no significant difference between median scores of perceptions about AI in medicine in terms of gender, age, year of medical study, having a background in mathematics, statistics, or computer science, having a good experience in technology, having a parent or sibling with an AI major, and type of university.

    “there was no significant difference between the median scores in terms of gender, age, year of medical study, having a background in mathematics, statistics, or computer science, having a good experience in technology, having a parent or sibling with an AI major, and type of university they are studying at.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    A small percentage of participants said that including AI in the medical curriculum should not be a top priority, even though the majority of participants thought it was vital.

    “A small percentage of participants said that including AI in the medical curriculum should not be a top priority, even though the majority of participants thought it was vital.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    A significant difference was found in mean perception scores concerning gender (p < 0.001), with male students (3.15 ± 0.87) having higher perception scores than female students (2.81 ± 0.86).

    “A significant difference was found in the mean perception scores concerning gender (p < 0.001). Male students (3.15 ± 0.87) had higher perception scores than female students (2.81 ± 0.86).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The most highly rated methods of learning about AI in medicine were workshops (106, 30.37%), extracurricular activities (104, 29.8%), lectures (92, 26.36%), and participation in scientific activities with other departments (14, 4.01%).

    “The most highly rated methods of learning about AI in medicine were workshops (106, 30.37%), extracurricular activities (104, 29.8%), lectures (92, 26.36%), as well as participation in scientific activities with other departments (mathematics, computer science) (14, 4.01%).”
    Quote in the paperNot a substantial claim

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

    Findingown data

    Participants revealed a variety of emotions towards AI; some reported optimism for the future of technology, while others voiced concerns that it would be misused.

    “Participants revealed a variety of emotions towards AI. Some reported their optimism for the future of technology, while others voiced concerns that it would be misused.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The qualitative analysis of interview questions yielded four main themes: absence of AI learning opportunities, necessity of including AI in medical curricula, optimism towards the future of AI in medicine, and expected challenges related to AI in medical fields.

    “The main themes that resulted from the qualitative analysis of the interview questions were an absence of AI learning opportunities, the necessity of including AI in medical curricula, optimism towards the future of AI in medicine, and expected challenges related to AI in medical fields.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Findingnone given

    There is a general weakness among medical students in the basics of computer science, mathematics, and programming.

    “Technological illiteracy plays an important role in this problem, as there is a general weakness in the basics of computer science, mathematics, and programming.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingreasoning

    Some participants thought AI would revolutionize medicine but also thought it would not directly impact their medical careers.

    “Some thought AI would revolutionize medicine but also thought it would not directly impact their medical careers.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Qualitative analysis of interview questions revealed four main themes: an absence of current AI learning opportunities, the necessity of including AI learning in medical curricula, optimism towards the future of AI in medicine, and expected challenges related to learning AI in medical fields by undergraduate medical students.

    “The qualitative analysis of interview questions revealed four main themes: an absence of current AI learning opportunities, the necessity of including AI learning in medical curricula, optimism towards the future of AI in medicine, and expected challenges related to learning AI in medical fields by undergraduate medical students (Table 7).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Extracurricular activities (40.7%, 142), collaborative activities (29.8%, 104), lectures (12.6%, 44), conferences (7.7%, 27), and workshops (5.2%, 18) were favorite formats for learning and getting more information about AI in medicine.

    “Extracurricular activities (142, 40.7%), collaborative activities (104, 29.8%), lectures (44, 12.6%), conferences (27, 7.7%), and workshops (18, 5.2%) were favorite formats for learning and getting more information about AI in medicine”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    113 participants (32.38%) were aware of the applications of AI in daily life, whereas 236 (67.62%) were not aware.

    “The study revealed that 113 (32.38%) participants were aware of the applications of AI in daily life, whereas about two-thirds were not aware (236, 67.62%).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Participants reported social, economic, ethical, technological, political, institutional, and information challenges.

    “The participants reported social, economic, ethical, technological, political, institutional, and information challenges.”
    Quote in the paperSubstantial claim

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

    Study design and conductnone given

    The study was prepared according to the Checklist for Reporting of Survey Studies (CROSS).

    “This study was prepared according to the Checklist for Reporting of Survey Studies (CROSS) (Supplementary File 1).”
    Quote in the paperNot a substantial claim

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

    Study design and conductreasoning

    The sample size of 371 was believed to be adequate for the study's target population.

    “This sample size is believed to be adequate for the study’s target population.”
    Quote in the paperNot a substantial claim

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

    Study design and conductreasoning

    Duplication of respondents was prevented by email address, and the statistician checked the email address list to ensure there were no duplicate responses from the same participant.

    “We prevented duplication of respondents by email address. We did not allow access to the questionnaire without an email address, so the participants had only one access to fill out the questionnaire. Also, the statistician checked out the email address list to ensure that there were no duplicate responses from the same participant.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductcitation

    The questionnaire dimensions and questions were developed from a survey designed by Pucchio et al. that had been validated through a study of the literature and discussions with medical professionals and health experts.

    “We developed the questionnaire dimensions and questions from the survey designed by Pucchio et al. [40], who validated the survey through a study of the literature and discussions with medical professionals and health experts.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [40] Pucchio A, Rathagirishnan R, Caton N, Gariscsak PJ, Del Papa J, Nabhen JJ, Vo V, Lee W, Moraes FY. Exploration of exposure to artificial intelligence in undergraduate medical education: a Canadian cross-sectional mixed-methods study. BMC Med Educ. 2022;22(1):815. source

      Not checked in the free run.

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

    Study design and conductcitation

    The interviews were conducted in compliance with the Consolidated Criteria for Reporting Qualitative Research (COREQ) standards.

    “The interviews were conducted in compliance with the standards for reporting qualitative research set by the Consolidated Criteria for Reporting Qualitative Research (COREQ) [44].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [44] Tong A, Sainsbury P, Craig J. Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care. 2007;19(6):349–57. source

      Not checked in the free run.

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

    Interpretationown data

    Misunderstanding and lack of knowledge are factors that contribute to the expansion of the gap and complicate the situation among undergraduate medical students.

    “Misunderstanding and lack of knowledge are factors that contribute to the expansion of the gap and complicate the situation among undergraduate medical students.”
    Quote in the paperNot a substantial claim

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

    Premisecitation

    AI has the potential to be a helpful medical tool in diagnostics and clinical decision-making due to its capacity to integrate vast volumes of clinical data.

    “AI has the potential to be a helpful medical tool in diagnostics and clinical decision-making due to its excellent capacity to integrate vast volumes of clinical data [3, 4].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [3] Amato F, López A, Peña-Méndez EM, Vaňhara P, Hampl A, Havel J. Artificial neural networks in medical diagnosis. J Appl Biomed. 2013;11(2):47–58. source

      Not checked in the free run.

    • cites [4] Bennett CC, Hauser K. Artificial intelligence framework for simulating clinical decision-making: a Markov decision process approach. Artif Intell Med. 2013;57(1):9–19. source

      Not checked in the free run.

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

    Interpretationcitation

    The higher perception scores among male students may be attributed to male students having more information and adequate knowledge related to AI than female students.

    “This result may be attributed to the fact that male students have more information and adequate knowledge related to AI than female students [48, 60].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [48] Minh NT, Vo TQ, Tran HT, Nguyen HT. Healthcare students’ knowledge, attitudes, and perspectives toward artificial intelligence in the southern Vietnam. Heliyon. 2023;9:e22653. source

      Not checked in the free run.

    • cites [60] Alghamdi SA, Alashban Y. Knowledge, attitudes and practices towards artificial intelligence (AI) among radiologists in Saudi Arabia. J Radiation Res Appl Sci. 2023;16(2):100569.

      Not checked in the free run.

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

    Study design and conductown data

    Thirteen participants were excluded from the analysis due to incomplete responses.

    “Thirteen participants have been excluded from the analysis due to their incomplete responses (partially completed survey).”
    Quote in the paperNot a substantial claim

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

    Study design and conductnone given

    Data collection continued until saturation was reached and no new topics started in the interviews.

    “The collection of data was continued until it reached saturation and no new topics started in the interviews.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    The absence of formal AI programs in Palestinian universities is due to challenges including lack of funding and absence of material and technical support.

    “This is due to the many challenges that Palestinian universities face, the most important of which is the lack of funding and the absence of material and technical support.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Study design and conductown data

    From 371 invitations sent, 362 responses were received (response rate 97.5%), and 349 were included in the analysis.

    “From a total of 371 invitations sent, 362 responses were received (response rate = 97.5%), and 349 were included in the analysis (Fig. 1).”
    Quote in the paperNot a substantial claim

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

    Premisecitation

    AI has an impact on medical education by providing medical students with an interactive learning environment and creating virtual simulations and training.

    “As evidenced by the COVID-19 pandemic, AI has an impact on medical education where it provides medical students with an interactive learning environment and creates virtual simulations and training, allowing them to practice complicated procedures on virtual patients without endangering actual patients [17–20].”
    Quote in the paperNot a substantial claim0 of 4 sources checked
    • cites [17] Suh I, McKinney T, Siu KC. Current perspective of Metaverse Application in Medical Education, Research and Patient Care. InVirtual Worlds. 2023;2(2):115–28. source

      Not checked in the free run.

    • cites [18] Malhotra K, Wong BN, Lee S, Franco H, Singh C, Silva LA, Iraqi H, Sinha A, Burger S, Breedt DS, Goyal K. Role of Artificial Intelligence in global surgery: a Review of opportunities and challenges. Cureus. 2023;15(8):e43192.

      Not checked in the free run.

    • cites [19] Dave M, Patel N. Artificial intelligence in healthcare and education. Br Dent J. 2023;234(10):761–4. source

      Not checked in the free run.

    • cites [20] Gomes RF, Schmith J, Figueiredo RM, Freitas SA, Machado GN, Romanini J, Carrard VC. Use of artificial intelligence in the classification of elementary oral lesions from clinical images. Int J Environ Res Public Health. 2023;20(5):3894. source

      Not checked in the free run.

  • The quote only partly states this claim.

    Interpretationown data

    Medical students lack access to educational opportunities for AI in medicine.

    “Medical students lack access to educational opportunities for AI in medicine; therefore, AI should be included in formal medical curricula in Palestine.”
    Quote in the paperSubstantial claim

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

    Premisecitation

    Many educational institutions struggle to effectively integrate AI into their teaching practices due to a lack of teachers qualified in AI, the expensive cost of AI software, and ethical issues.

    “However, due to a lack of teachers qualified in AI, the expensive cost of AI software, and ethical issues, many educational institutions struggle to effectively integrate AI into their teaching practices [21, 22].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [21] Thurzo A, Strunga M, Urban R, Surovková J, Afrashtehfar KI. Impact of artificial intelligence on dental education: a review and guide for curriculum update. Educ Sci. 2023;13(2):150. source

      Not checked in the free run.

    • cites [22] Sedaghat S. Early applications of ChatGPT in medical practice, education and research. Clin Med. 2023;23(3):278–9. source

      Not checked in the free run.

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

    Study design and conductnone given

    There were no missing data.

    “There were no missing data.”
    Quote in the paperNot a substantial claim

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

    Study design and conductnone given

    The study was approved by the Ethics Committee of the Faculty of Science of the Palestine Technical University, Kadoorie, Hebron, Palestine (App. ID 10-2022).

    “The present study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of the Faculty of Science of the Palestine Technical University, Kadoorie, Hebron, Palestine (App. ID 10-2022).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    The inconsistencies among participants can be attributed to lack of knowledge of AI applications, misconceptions around AI perpetuated by the media, and lack of practical exposure to AI in a medical environment.

    “These results can be attributed to several reasons, such as the lack of knowledge of AI applications, misconceptions around AI perpetuated by the media, and a lack of practical exposure to AI in a medical environment.”
    Quote in the paperNot a substantial claim

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

    Study design and conductown data

    All participants provided informed consent before completing the questionnaire.

    “All participants provided informed consent to participate before completing the questionnaire.”
    Quote in the paperNot a substantial claim

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

    Study design and conductcitation

    To verify rigor, the study used Lincoln and Guba's criteria for confirmability, credibility, dependability, and transferability.

    “To verify the rigor of this study, we used Lincoln and Gube’s [45] criteria for confirmability, credibility, dependability, and transferability.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [45] Lincoln YS, Guba EG. Naturalistic inquiry. sage; 1985. source

      Not checked in the free run.

  • May combine two claims.

    Study design and conductown data

    The study's sample size is not representative of all governorates of Palestine, where most of the participants were from the West Bank.

    “This study’s sample size is not representative of all governorates of Palestine, where most of the participants were from the West Bank.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

    Interpretationreasoning

    Training the next generation of medical students in how AI will fit into clinical practices will help them make major changes in the medical field, enhancing the careful use of these instruments in manual practice and ultimately improving the healthcare system.

    “Given that AI instruments are likely to be extensively utilized in the future, training the next generation of medical students in how AI will fit into clinical practices will help them make major changes in the medical field, enhancing the careful use of these instruments in manual practice and ultimately improving the healthcare system.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: AI instruments are likely to be extensively utilized in the future.

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

    Interpretationreasoning

    There is a dire need for the incorporation of AI courses in medical curricula.

    “As the participants had not previously acquired training in the employment of AI in medicine during formal education, this confirmed a dire need for the incorporation of AI courses in medical curricula.”
    Quote in the paperNot a substantial claim

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

    Interpretationown data

    AI curriculum building can be achieved in a well-informed manner in Palestine due to insights provided by this study and results regarding preferred AI education formats and obstacles to AI education.

    “AI curriculum building can be achieved in a well-informed manner in Palestine due to insights provided by this study and results regarding preferred AI education formats and obstacles to AI education.”
    Quote in the paperNot a substantial claim

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

    Study design and conductnone given

    The study lacked sensitivity analyses.

    “Finally, this study lacked sensitivity analyses.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductown data

    The study sample was non-representative, mainly female (71.92%), which may limit the generalizability of the findings to a wider population.

    “Limitations of this study include the non-representativeness of the study sample, which mainly consisted of female students (71.92%). This may limit the generalizability of our findings to a wider population.”
    Quote in the paperNot a substantial claim
39 of 39

  • The paper relies on this without support.

    There is a knowledge gap in Palestine because no recent studies have investigated Palestinian medical students’ perceptions about the integration of AI in medical education.

    “An evident knowledge gap is present in this field in Palestine, which must be addressed due to the inevitable future increase in AI use in the medical field.”
    “However, based on our knowledge, there are no recent studies investigating the perceptions of Palestinian medical students about the integration of AI in medical education.”

  • The paper relies on this without support.

    AI use in the medical field will inevitably increase in the future.

    “An evident knowledge gap is present in this field in Palestine, which must be addressed due to the inevitable future increase in AI use in the medical field.”

  • The paper relies on this without support.

    AI is quickly advancing and its usage in medical fields worldwide is increasing.

    “given the quick advancement and the increasing usage of AI in the medical fields around the world”

  • The paper relies on this without support.

    The skills needed to use AI may differ from those traditionally possessed by medical students.

    “as the skills needed to use AI may differ from those traditionally possessed by medical students”

  • The paper relies on this without support.

    Teaching medical students the basics of computer science, mathematics, and programming before incorporating AI into the medical curriculum requires a longer time (two semesters).

    “this in turn requires a longer time (two semesters)”

  • The paper relies on this without support.

    Students who join workshops and attend courses on AI in medicine will become educated and have more knowledge of AI and higher perception and awareness of AI-related topics.

    “students who join workshops and attend courses on AI in medicine will become educated and have more knowledge of AI; therefore, they will have a higher perception and awareness of AI-related topics.”

  • The paper relies on this without support.

    AI instruments are likely to be extensively utilized in the future.

    “Given that AI instruments are likely to be extensively utilized in the future”

  • The paper relies on this without support.

    No previous Palestinian study focused on the perceptions of medical students about AI in medicine.

    “Based on our knowledge, no previous Palestinian study focused on the perceptions of medical students about AI in medicine.”
8 of 8

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?

  • Qualitative insights from Palestine and Ireland highlight students' preference for interactive, practice-oriented learning experiences, such as AI-assisted simulations, to enhance hands-on competence.

    “Qualitative insights from Palestine [49] and Ireland [34] further highlight students’ preference for interactive, practice-oriented learning experiences, such as AI-assisted simulations, to enhance hands-on competence.”

    From Is it time to introduce an artificial intelligence curriculum in undergraduate medical education? medical students’ perspectives: a systematic review and meta-analysis (2026) · cites this paper as [49]

    The Results and Discussion (pages unavailable in our copy) report that Palestinian students favored workshops, collaborative activities, and extracurricular learning, and one student mentioned an AI simulation tool. They do not establish that students preferred AI-assisted simulations as a learning format or that such simulations enhanced hands-on competence. The Ireland finding is attributed to a different citation and is not assessed here. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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.

  • Interview sampling is described as both random and purposive

    Major

    The account of recruiting interviewees calls them a random sample, while the qualitative-study methods say purposive sampling was used at that stage.

  • Table 6 places non-count values in the age-group sample-size column

    Minor

    Table 6 labels its second column n(%), but its age rows contain means and standard deviations there. Table 2 instead gives the age-group counts as 140 and 209.

  • Table 5 reverses the computer-science background counts

    Minor

    Table 2 reports 115 participants with this background and 234 without it. Table 5 assigns those same counts to the opposite answers; Table 6 repeats the reversal.

  • Discussion calls matching findings inconsistent

    Minor

    The discussion says its students were not concerned about AI developments, then calls a study finding that the issue was not a concern inconsistent with its results.

  • Subgroup tables reverse the technological-literacy counts

    Minor

    Table 2 reports 131 participants answering yes and 218 answering no. Table 5 assigns 218 to yes and 131 to no, a reversal also present in Table 6.

  • An interviewee is described as fourth-year despite no fourth-year interviewees being listed

    Minor

    The breakdown of all fifteen interviewees includes first-, second-, third-, and fifth-year students only. A later interview account identifies a student in their fourth year.

  • Subgroup tables reverse the counts for relatives with AI degrees

    Minor

    Table 2 reports 89 participants with a parent or sibling holding an AI-related degree and 260 without one. Table 5 assigns those counts to the opposite answers; Table 6 does likewise.

  • Table 5 age-group means cannot match its gender-group means

    Minor

    The two age groups cover all participants and have reported means of 2.88 and 2.93. The two gender groups also cover all participants but have means of 3.20 and 3.31. Both partitions cannot describe the same total perception score.

8 of 8

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  1. Alghamdi SA, Alashban Y. Knowledge, attitudes and practices towards artificial intelligence (AI) among radiologists in Saudi Arabia. J Radiation Res Appl Sci. 2023;16(2):100569.

Cited by

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Related

Authors
Jebreen, K., Radwan, E., Kammoun-Rebai, W., Alattar, E., Radwan, A., Safi, W., Radwan, W., & Alajez, M.
Affiliations
  • An-Najah National University (PS)
  • Islamic University of Gaza (PS)
  • University College of Applied Science (PS)
  • Al-Azhar University – Gaza (PS)
Corresponding author
Radwan, Eqbal
ORCID
Venue
BMC Medical Education
Publisher
Springer Science and Business Media LLC
Published
2024-05-07
Volume
24
Issue
1
Pages
507-507
ISSN
1472-6920 (ISSN-L)
DOI
10.1186/s12909-024-05465-4
PMC
PMC11077786
OpenAlex
W4396691973
Semantic Scholar
1945bfa6a2989235cd9aa7ead469299f7cc9ad8e
Cited by
63
References
68
Citations by year
2026: 22 · 2025: 31 · 2024: 10
FWCI
5.89
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Medical Education (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • 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, medical students' attitudes, artificial intelligence in medical education, Palestine, medical education
MeSH terms
Adult, Artificial Intelligence, Attitude of Health Personnel, Cross-Sectional Studies, Curriculum, Education, Medical, Undergraduate, Female, Humans, Male, Middle East, Surveys and Questionnaires, Students, Medical, Arabs, Qualitative Research, Young Adult

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