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Cited by 51Artificial Intelligence in Healthcare and EducationArtificial Intelligence in EducationInnovations in Medical Education

ChatGPT and Clinical Training: Perception, Concerns, and Practice of Pharm-D Students

Open access
Zawiah, Mohammed, Al-Ashwal, Fahmi, Gharaibeh, Lobna, Abu Farha, Rana, Alzoubi, Karem, Abu Hammour, Khawla, Qasim, Qutaiba A, Abrah, Fahd·2023·Journal of Multidisciplinary Healthcare
DOI 10.2147/jmdh.s439223PMC PMC10729768
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Abstract

Background: The emergence of Chat-Generative Pre-trained Transformer (ChatGPT) by OpenAI has revolutionized AI technology, demonstrating significant potential in healthcare and pharmaceutical education, yet its real-world applicability in clinical training warrants further investigation. Methods: A cross-sectional study was conducted between April and May 2023 to assess PharmD students' perceptions, concerns, and experiences regarding the integration of ChatGPT into clinical pharmacy education. The study utilized a convenient sampling method through online platforms and involved a questionnaire with sections on demographics, perceived benefits, concerns, and experience with ChatGPT. Statistical analysis was performed using SPSS, including descriptive and inferential analyses. Results: The findings of the study involving 211 PharmD students revealed that the majority of participants were male (77.3%), and had prior experience with artificial intelligence (68.2%). Over two-thirds were aware of ChatGPT. Most students (n= 139, 65.9%) perceived potential benefits in using ChatGPT for various clinical tasks, with concerns including over-reliance, accuracy, and ethical considerations. Adoption of ChatGPT in clinical training varied, with some students not using it at all, while others utilized it for tasks like evaluating drug-drug interactions and developing care plans. Previous users tended to have higher perceived benefits and lower concerns, but the differences were not statistically significant. Conclusion: Utilizing ChatGPT in clinical training offers opportunities, but students' lack of trust in it for clinical decisions highlights the need for collaborative human-ChatGPT decision-making. It should complement healthcare professionals' expertise and be used strategically to compensate for human limitations. Further research is essential to optimize ChatGPT's effective integration.

AI snapshot

Written from the paper’s full text.

TL;DR

This cross-sectional survey of 211 Jordanian PharmD students found that most perceived potential benefits of ChatGPT for clinical training, especially for treatment-related problems and drug-drug interactions, but many had concerns about accuracy, over-reliance, ethics, and accessibility. Adoption was limited, and prior users reported higher perceived benefits and lower concerns, though not statistically significant.

Key findings

  • Among 211 PharmD students, 68.2% had prior AI experience and 68.7% were aware of ChatGPT; 65.9% (n=139) believed PharmD students could benefit from ChatGPT, while only 39.8% believed it could provide accurate clinical practice information.
  • Perceived benefits included expediting identification/management of treatment-related problems (57.3%, n=121), drug-drug interactions (54.5%, n=115), and adverse drug reactions (50.2%, n=106).
  • Concerns included over-reliance/critical thinking, accuracy/reliability, and contextual understanding (each 61.1%, n=129), accessibility/digital divide (58.8%, n=124), ethical/privacy issues (51.2%, n=108), and bias in training data (50.2%, n=106).
  • Adoption varied: 46.4% (n=98) never used ChatGPT in clinical training, 12.8% (n=27) used it frequently, 53.1% (n=112) were somewhat/very likely to use it in future practice, and 63% (n=123) would recommend it; among 113 ever-users, 72.6% used it for drug/disease information and 64.6% for drug-drug interactions.

Methods

  • Cross-sectional online questionnaire conducted April–May 2023 among PharmD students at the two public pharmacy schools in Jordan, using convenient sampling via Facebook and WhatsApp; 211 students participated, above the minimum calculated sample size of 86.
  • The questionnaire had four parts: demographics, perceived benefits (16 items), concerns (8 items), and experience with ChatGPT; benefits and concerns were rated on a 5-point Likert scale, with stated score ranges of 16–80 for benefits and 9–45 for concerns.
  • Analysis used SPSS version 25; normality was assessed with Kolmogorov–Smirnov and histograms, showing non-normal distribution; Mann–Whitney U-test compared users and non-users, with p<0.05 significant, and Table 6 notes Kruskal–Wallis tests for future intent and recommendation.

Limitations

  • The paper states it did not examine the effect of ChatGPT use on the accuracy of students’ clinical decisions or patient outcomes; convenience and snowball sampling may introduce selection bias; and social media recruitment prevents calculation of a survey response rate.
  • Evident inconsistencies in the text include gender reporting: the narrative says the majority were male (n=163, 77.3%), while Table 1 lists male as 48 (22.7%) and female as 163 (77.3%).
  • Additional evident issues include Table 2 being labeled N=221 despite the study sample of 211, and the concern score range being described as 9–45 for 8 items.

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

Integrity signals

Registry

Crossref · 10.2147/jmdh.s439223

Cites

31 of 31 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 3 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    38.9% (n=82) of participants agreed or strongly agreed that ChatGPT can assist in identifying wrong medications prescribed to the patient.

    “Chat GPT can assist in identifying wrong medications prescribed to the patient. | 82 (38.9) | 73 (34.6) | 56 (26.5)”
    Quote in the paperNot a substantial claim
  • Findingown data

    50.2% (n=106) of participants agreed or strongly agreed that ChatGPT may have bias in its training data, resulting in biased responses or recommendations.

    “Bias in data: There is a possibility that ChatGPT may have bias in its training data, which could result in biased responses or recommendations. | 106 (50.2) | 64 (30.3) | 41 (19.4)”
    Quote in the paperNot a substantial claim
  • Findingown data

    46.0% (n=97) of participants agreed or strongly agreed that ChatGPT can assist in therapeutic drug monitoring.

    “Chat GPT can assist in therapeutic drug monitoring. | 97 (46.0) | 69 (32.7) | 45 (21.3)”
    Quote in the paperNot a substantial claim
  • Findingown data

    68.7% (n=145) of participants were aware of ChatGPT.

    “over two-thirds of the participants (n= 145, 68.7%) were aware of ChatGPT.”
    Quote in the paperNot a substantial claim
  • Findingown data

    46.4% (n=98) of participants agreed or strongly agreed that ChatGPT can assist in clinical pharmacokinetic calculations.

    “Chat GPT can assist in clinical pharmacokinetic calculations. | 98 (46.4) | 64 (30.3) | 49 (23.2)”
    Quote in the paperNot a substantial claim
  • Classified as probably not a substantial claim. 16
  • May combine two claims. 11

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

    Findingown data

    85.3% (n=180) of participants indicated they have good to excellent proficiency in computer and digital technology usage.

    “the majority (n= 180, 85.3%) indicating they have good to excellent proficiency.”
    Quote in the paperNot a substantial claim

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

    Findingown data

    68.2% (n=144) of participants had prior experience with artificial intelligence.

    “over two-thirds of participants (n= 144, 68.2%) had prior experience with artificial intelligence.”
    Quote in the paperNot a substantial claim

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

    Findingown data

    49.8% (n=105) of participants were from the Jordan University of Science and Technology.

    “49.8% (n= 105) from the Jordan University of Science and Technology”
    Quote in the paperNot a substantial claim

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

    Findingown data

    50.2% (n=106) of participants were from the University of Jordan.

    “50.2% (n= 106) from the University of Jordan”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    61.1% (n=129) of participants agreed or strongly agreed that students may become overly reliant on ChatGPT and may not develop necessary critical thinking and problem-solving skills.

    “Dependence on technology: Students may become overly reliant on ChatGPT and may not develop the necessary critical thinking and problem-solving skills that are essential for the practice of pharmacy | 129 (61.1) | 48 (22.7) | 34 (16.1)”
    Quote in the paperNot a substantial claim

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

    Findingown data

    The majority of participants were male (n=163, 77.3%).

    “the majority were males (n= 163, 77.3%)”
    Quote in the paperNot a substantial claim

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

    Findingown data

    58.8% (n=124) of participants agreed or strongly agreed that some students may not have access to necessary technology or resources to effectively use ChatGPT, creating disparities in learning opportunities.

    “Accessibility: Some students may not have access to the necessary technology or resources to effectively use ChatGPT in their training, which could create disparities in learning opportunities. | 124 (58.8) | 54 (25.6) | 33 (24.0)”
    Quote in the paperNot a substantial claim

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

    Findingown data

    The median age of the 211 participants was 23 years (IQR=1.0).

    “The median age of participants was 23 years (IQR= 1.0)”
    Quote in the paperNot a substantial claim

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

    Findingown data

    Over half of the respondents were sixth-year students (n=120, 56.9%).

    “over half of the respondents were sixth-year students (n= 120, 56.9%)”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Participants more inclined to use ChatGPT in future clinical practice tended to have lower concern scores, with a statistically significant correlation (p=0.038 for those 'Somewhat likely' to use).

    “However, participants more inclined to use ChatGPT in future clinical practice tended to exhibit lower concern scores, showing a statistically significant correlation (p-value of 0.038 for those “Somewhat likely” to use).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Previous ChatGPT users had lower perceived concerns scores than non-users, but the difference was not statistically significant (P>0.05).

    “When comparing previous experience in using ChatGPT with their perceived benefit and concern scores (Table 6), previous users showed higher perceived benefit perception scores and lower perceived concerns scores compared to non-users, but these differences were not statistically significant (P>0.05 for both).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Two-thirds of participants believed ChatGPT offers them benefits, especially in identification of treatment-related problems, drug-drug interactions, and adverse drug reactions.

    “Results revealed that two-thirds of the participants believed that ChatGPT offers them benefits, especially in the identification of treatment-related problems, drug-drug interactions, and adverse drug reactions participants.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Participants expressed skepticism about ChatGPT's accuracy in clinical information provision, with less than half believing it could produce accurate information.

    “The accuracy of the data provided by the ChatGPT was questionable, and less than half of the participants believed it could produce accurate information.”
    • Also stated in: § Conclusion · plain
      “Participants acknowledged potential benefits in expediting treatment-related problem identification and drug interactions but expressed skepticism about its accuracy in clinical information provision.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Previous ChatGPT users had higher perceived benefit scores than non-users, but the difference was not statistically significant (P>0.05).

    “When comparing previous experience in using ChatGPT with their perceived benefit and concern scores (Table 6), previous users showed higher perceived benefit perception scores and lower perceived concerns scores compared to non-users, but these differences were not statistically significant (P>0.05 for both).”
    Quote in the paperNot a substantial claim

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

    Findingcitation

    Many participants agreed that ChatGPT can be helpful in providing patient education.

    “Many participants agreed that ChatGPT can be helpful in providing patient education.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Significant concerns included over-reliance, digital divide implications, compromised personal interaction, and ethical considerations.

    “Significant concerns included over-reliance, digital divide implications, compromised personal interaction, and ethical considerations.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Previous users of ChatGPT tended to have higher perceived benefits and lower concerns than non-users, but the differences were not statistically significant.

    “Previous users tended to have higher perceived benefits and lower concerns, but the differences were not statistically significant.”
    Quote in the paperNot a substantial claim

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

    Study design and conductown data

    The study questionnaire was evaluated for content and face validity by two survey experts.

    “The initial study questionnaire was generated and evaluated for content and face validity by two survey experts.”
    Quote in the paperNot a substantial claim

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

    Study design and conductnone given

    Ethical approval for the study was granted by the Institutional Review Board at the Jordan University Hospital (Approval number: 10/2023/16,283).

    “Ethical approval for the study was granted by the Institutional Review Board at the Jordan University Hospital (Approval number: 10/2023/16,283).”
    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 conducted following the Declaration of Helsinki of the World Medical Association.

    “The current study was conducted following the Declaration of Helsinki of the World Medical Association.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductown data

    Data normality was examined using the Kolmogorov-Smirnov test and histogram visualizations, revealing a non-normal distribution (p < 0.05).

    “The data normality was examined using the Kolmogorov–Smirnov test and histogram visualizations, revealing a non-normal distribution. The test yielded a significant result (p < 0.05), affirming this non-normal distribution.”
    Quote in the paperSubstantial claim

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

    Premisecitation

    The integration of AI-powered tools into clinical pharmacy practices could bolster efficiency, decision-making, and patient outcomes.

    “The integration of AI-powered tools into clinical pharmacy practices could bolster efficiency, decision-making, and patient outcomes.9”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [9] Liu, 2023, J Am Med Inform Assoc source

      Not checked in the free run.

  • May combine two claims.

    Study design and conductnone given

    Data collection relied on convenience and snowball sampling techniques, which may introduce selection bias.

    “Also, data collection relied on convenience and snowball sampling techniques, which may introduce selection bias.”
    Quote in the paperNot a substantial claim

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

    Interpretationcitation

    Communication with patients seems to be an area where ChatGPT can be very helpful.

    “Communication with patients seems to be an area where ChatGPT can be very helpful.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    The correlations suggest a link between users' attitudes towards ChatGPT, their perceived benefits, and concerns, potentially influencing their inclination to use or recommend it in clinical practice.

    “These correlations suggest a link between users’ attitudes towards ChatGPT, their perceived benefits, and concerns, potentially influencing their inclination to use or recommend it in clinical practice.”
    Quote in the paperSubstantial claim

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

    Study design and conductnone given

    The study did not examine the effect of ChatGPT utilization by students on the accuracy of their clinical decisions or patient outcomes.

    “However, one limitation of the study is that it did not examine the effect of ChatGPT utilization by students on the accuracy of their clinical decisions or patient outcomes.”
    Quote in the paperNot a substantial claim

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

    Study design and conductnone given

    Utilizing social media for participant recruitment prevents the calculation of a survey response rate.

    “Moreover, utilizing social media for participant recruitment prevents the calculation of a survey response rate.”
    Quote in the paperNot a substantial claim
27 of 27

  • The paper relies on this without support.

    The integration of ChatGPT into pharmacy practice training remains to be investigated.

    “Yet, the integration of ChatGPT to pharmacy practice training remains to be investigated.”
1 of 1

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.

  • Reported majority-male sample conflicts with the gender table

    Major

    The text says 163 participants were male, but Table 1 assigns 163 participants to the female category and 48 to the male category.

  • Incorrect percentage for disagreement about unnecessary medications

    Minor

    The reported 60 students represent 28.4% of the 211 participants, not 17.2%.

    Reported 17.2 · recomputed 28.436019

  • Recommendation count does not match its table categories

    Minor

    Table 4 lists 39 students who would definitely recommend ChatGPT and 94 who would probably recommend it, totaling 133 rather than the stated 123. The stated 63% is consistent with 133 of 211.

    Reported 123 · recomputed 133

  • Incorrect percentage for those not at all likely to use ChatGPT

    Minor

    Fifteen of 211 participants represent 7.1%, not 7.3%.

    Reported 7.3 · recomputed 7.109005

  • Incorrect percentage for adverse-drug-reaction use

    Minor

    Among 113 previous users, 74 represent 65.5%, not the 64.5% printed in Table 5. The accompanying text gives 65.5%.

    Reported 64.5 · recomputed 65.486726

  • Therapeutic-drug-monitoring use differs between text and table

    Minor

    The text reports 67 of 113 users, while Table 5 reports 66 users for the same task.

  • Incorrect accessibility-concern percentage

    Minor

    The 33 students in the disagreement category represent 15.6% of 211, not 24.0%.

    Reported 24 · recomputed 15.63981

  • Table 2 gives a different sample size

    Minor

    Table 2 is labeled N=221, whereas the study reports 211 students. Its first row also contains 211 responses.

  • Concern-score maximum does not match eight five-point items

    Minor

    Eight items scored from 1 to 5 would have a total-score range of 8 to 40, not the stated 9 to 45.

    Reported 45 · recomputed 40

9 of 9

Cites

Flagged first, then by citations

Research works printed but not linked: 8

  1. Huang, 2023, Br J Clin Pharmacol, 2023, 1
  2. Wang, 2023, J Chin Med Assoc, 10, 1097
  3. Zong, 2023, medRxiv, 2023, 23292415
  4. Hammour, 2023, J Pharm Policy Pract, 16
  5. Liu, 2023, medRxiv, 2023, 1
  6. Abouammoh, 2023, medRxiv, 2023, 23292624
  7. Johnson, 2023, Res Sq, 2023, 1
  8. Sallam, 2023, Cureus, 15, 2

Cited by

Sorted by citations

Related

Authors
Zawiah, M., Al-Ashwal, F., Gharaibeh, L., Abu Farha, R., Alzoubi, K., Abu Hammour, K., Qasim, Q. A., & Abrah, F.
Affiliations
  • Northern Border University (SA)
  • Al-Ayen Iraqi University (IQ)
  • Al-Ahliyya Amman University (JO)
  • Jordan University of Science and Technology (JO)
  • Universiti Sains Malaysia (MY)
Corresponding author
Zawiah, Mohammed
ORCID
Venue
Journal of Multidisciplinary Healthcare
Publisher
Informa UK Limited
Published
2023-12
Volume
Volume 16
Pages
4099-4110
ISSN
1178-2390 (ISSN-L)
DOI
10.2147/jmdh.s439223
PMC
PMC10729768
OpenAlex
W4389795250
Semantic Scholar
373e695c19f4ea775935ed400bb59b0076114658
Cited by
51
References
31
Citations by year
2026: 15 · 2025: 22 · 2024: 13 · 2023: 1
FWCI
2.04
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Multidisciplinary Healthcare (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • Dove Medical Press (Taylor and Francis Group) (submittedVersion, cc-by-nc)
Funders
Northern Border University
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
Keywords
ChatGPT, large language models, clinical pharmacy education, PharmD students, student perceptions, overreliance on AI, trust in AI, ethical considerations, care planning, human-AI collaboration

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