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Cited by 179Artificial Intelligence in Healthcare and EducationArtificial Intelligence ApplicationsArtificial Intelligence in Healthcare

ChatGPT and the Future of Digital Health: A Study on Healthcare Workers’ Perceptions and Expectations

Open access
Temsah, Mohamad-Hani, Aljamaan, Fadi, Malki, Khalid H., Alhasan, Khalid, Altamimi, Ibraheem, Aljarbou, Razan, Bazuhair, Faisal, Alsubaihin, Abdulmajeed +11 more·2023·Healthcare
DOI 10.3390/healthcare11131812PMC PMC10340744
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Abstract

This study aimed to assess the knowledge, attitudes, and intended practices of healthcare workers (HCWs) in Saudi Arabia towards ChatGPT, an artificial intelligence (AI) Chatbot, within the first three months after its launch. We also aimed to identify potential barriers to AI Chatbot adoption among healthcare professionals. A cross-sectional survey was conducted among 1057 HCWs in Saudi Arabia, distributed electronically via social media channels from 21 February to 6 March 2023. The survey evaluated HCWs’ familiarity with ChatGPT-3.5, their satisfaction, intended future use, and perceived usefulness in healthcare practice. Of the respondents, 18.4% had used ChatGPT for healthcare purposes, while 84.1% of non-users expressed interest in utilizing AI Chatbots in the future. Most participants (75.1%) were comfortable with incorporating ChatGPT into their healthcare practice. HCWs perceived the Chatbot to be useful in various aspects of healthcare, such as medical decision-making (39.5%), patient and family support (44.7%), medical literature appraisal (48.5%), and medical research assistance (65.9%). A majority (76.7%) believed ChatGPT could positively impact the future of healthcare systems. Nevertheless, concerns about credibility and the source of information provided by AI Chatbots (46.9%) were identified as the main barriers. Although HCWs recognize ChatGPT as a valuable addition to digital health in the early stages of adoption, addressing concerns regarding accuracy, reliability, and medicolegal implications is crucial. Therefore, due to their unreliability, the current forms of ChatGPT and other Chatbots should not be used for diagnostic or treatment purposes without human expert oversight. Ensuring the trustworthiness and dependability of AI Chatbots is essential for successful implementation in healthcare settings. Future research should focus on evaluating the clinical outcomes of ChatGPT and benchmarking its performance against other AI Chatbots.

AI snapshot

Written from the paper’s full text.

TL;DR

This cross-sectional survey of 1057 healthcare workers in Saudi Arabia assessed early perceptions of ChatGPT within three months of its launch. Most saw potential value in healthcare but reported concerns about credibility, accuracy, and medicolegal implications.

Key findings

  • Among respondents, 18.4% had used ChatGPT for healthcare purposes, while 84.1% of non-users expressed interest in using AI Chatbots in the future; 75.1% were comfortable incorporating ChatGPT into healthcare practice.
  • Perceived usefulness included medical decision-making (39.5%), patient and family support (44.7%), medical literature appraisal (48.5%), and medical research assistance (65.9%); 76.7% believed ChatGPT could positively impact the future of healthcare systems.
  • The main barrier was concern about lack of credibility and unclear sources of information feeding AI Chatbots (46.9%), followed by harmful or incorrect medical decision recommendations (40.2%), difficulty accessing AI models at work (38.1%), and medicolegal implications (30.8%).
  • In adjusted analyses, belief that ChatGPT could improve patient outcomes was associated with higher odds of believing it could improve the future healthcare system (OR = 7.92, p < 0.001), as were comfort using ChatGPT for medical purposes (OR = 2.327, p < 0.001), prior healthcare use (OR = 1.902, p = 0.004), and familiarity (OR = 2.023, p < 0.001).

Methods

  • Cross-sectional electronic survey distributed via social media channels, email invitations, and personal contacts among healthcare workers in Saudi Arabia from 21 February to 6 March 2023; 1057 HCWs participated.
  • The survey was developed by a multidisciplinary team, refined using focus groups, and piloted among 20 HCWs; it had three parts covering knowledge/use, perceived obstacles, and demographics/computer literacy, with multiple-choice questions and no Likert scale or open-ended questions.
  • Analysis used descriptive statistics, multiple response dichotomies for multiple-option questions, and multivariate-adjusted odds ratios with 95% confidence intervals; significance was set at p < 0.05 using SPSS version 21.

Limitations

  • The paper states that as a cross-sectional survey, the study is subject to sampling bias, response bias, and recall bias; the sampling technique may lead to selection bias, and participants may not represent the entire healthcare worker population.
  • The paper states that response bias may occur from socially desirable answers, misunderstanding questions, or unmeasured confounding; almost half reported unfamiliarity with ChatGPT, which could introduce bias.
  • The paper states that regional information was not collected due to privacy concerns, the absence of total population size limits generalizability, the study included respondents from one country, and no qualitative techniques were included.

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

Integrity signals

Registry

Crossref · 10.3390/healthcare11131812

Cites

37 of 37 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 14 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    Of the surveyed healthcare workers, 18.4% had used ChatGPT for healthcare purposes.

    “Of the respondents, 18.4% had used ChatGPT for healthcare purposes, while 84.1% of non-users expressed interest in utilizing AI Chatbots in the future.”
    • Also stated in: § 3. Results · plain
      “Among the surveyed HCWs, only 18.4% reported using ChatGPT for healthcare purposes as of the time of the survey, while 81.6% did not (Figure 1).”
    Quote in the paperNot a substantial claim
  • Findingown data

    HCWs who were concerned about AI Chatbots potentially taking over human roles in healthcare practice were significantly more inclined to use them for healthcare purposes in the future (OR = 1.969, p = 0.018).

    “Interestingly, HCWs who were concerned about AI Chatbots potentially taking over human roles in healthcare practice were significantly more inclined to use them for healthcare purposes in the future (OR = 1.969, p = 0.018).”
    Quote in the paperNot a substantial claim
  • Findingown data

    76.7% of HCWs believed ChatGPT could positively impact the future of healthcare systems.

    “A majority (76.7%) believed ChatGPT could positively impact the future of healthcare systems.”
    • Also stated in: § 3. Results · plain
      “Most HCWs (76.7%) anticipate a positive effect; 17.7% anticipate a negative impact, and 5.6% anticipate no impact at all.”
    Quote in the paperSubstantial claim
  • Findingown data

    81.8% of participants expressed a triggered interest in learning more about ChatGPT and other AI Chatbots.

    “As a result of our survey, 81.8% of participants expressed a triggered interest in learning more about ChatGPT and other AI Chatbots.”
    Quote in the paperNot a substantial claim
  • Findingown data

    No correlation was found between HCWs' trust in AI Chatbots or ChatGPT and the potential for ChatGPT to improve healthcare systems.

    “However, no correlation between HCWs’ trust in AI Chatbots or more specifically ChatGPT and the potential for ChatGPT to improve healthcare systems was found.”
    Quote in the paperNot a substantial claim
  • May combine two claims. 11
  • Classified as probably not a substantial claim. 14

  • May combine two claims.

    Findingown data

    The study identified several obstacles and concerns about ChatGPT's use in healthcare, including lack of credibility and questioned source of information, medicolegal implications, resistance to its use, and concerns about patient confidentiality and personalized care.

    “Our study identified several obstacles and concerns about ChatGPT’s use in healthcare, including its lack of credibility and questioned source of information, medicolegal implications, resistance to its use, and concerns about patient confidentiality and personalized care.”
    • Also stated in: § 4.5. Medicolegal Implications of ChatGPT · plain
      “Notably, HCWs expressed concerns regarding the credibility of AI Chatbots and their potential medicolegal implications.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The survey responses accurately reflect the participants' true perceptions and experiences.

  • May combine two claims.

    Findingown data

    HCWs showed moderate to low levels of trust in ChatGPT's ability to generate medical decisions, and only a small proportion showed a high level of trust.

    “The current study revealed that HCWs showed moderate to low levels of trust in ChatGPT’s ability to generate medical decisions, and only a small proportion showed a high level of trust.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The survey responses accurately reflect the participants' true perceptions and experiences.

  • May combine two claims.

    Findingown data

    HCWs who were users of ChatGPT and those with more clinical experience, self-rated familiarity, and comfort level in using ChatGPT for medical purposes had a positive perception of its usefulness and higher levels of trust.

    “HCWs users of ChatGPT and those with more clinical experience, self-rated familiarity, and comfort level in using ChatGPT for medical purposes a positive perception of its usefulness and with higher levels of trust.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The survey responses accurately reflect the participants' true perceptions and experiences.

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

    Premisecitation

    The Artificial Intelligence Index Report 2023 ranked Saudi Arabia as one of the top three countries with a positive perception of AI products and services.

    “The recent Artificial Intelligence Index Report 2023 from Stamford University about the world countries’ acceptability ranked Saudi Arabia as one of the top three countries with a positive perception of AI products and services [10].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [10] HAI (2023, June 12). Artificial Intelligence Index Report 2023, Available online: https://aiindex.stanford.edu/wp-content/uploads/2023/04/HAI_AI-Index-Report_2023.pdf.

      Not checked in the free run.

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

    Premisecitation

    According to the Artificial Intelligence Index Report 2023, 76% of Saudi respondents agreed that products and services using AI have more benefits than drawbacks, making Saudi Arabia the second most positive country towards AI after China.

    “According to the Artificial Intelligence Index Report 2023 by Stanford University, 76% of Saudi respondents agreed that products and services using AI have more benefits than drawbacks, making it the second most positive country towards AI after China [10].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [10] HAI (2023, June 12). Artificial Intelligence Index Report 2023, Available online: https://aiindex.stanford.edu/wp-content/uploads/2023/04/HAI_AI-Index-Report_2023.pdf.

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    The minimum sample size required for the study was 463 HCWs, calculated from an estimated proportion of 50%, margin of error 0.05, 95% confidence, 80% power, and increased by 20% for non-response.

    “Estimating a proportion of 50% of HCWs using ChatGPT and a margin of error of 0.05, confidence level of 95%, and study power of 80%, the sample size required for this study was 386. To account for incomplete and non-responses, the sample size was increased by 20%, making the minimum sample size required 463 HCWs.”
    Quote in the paperSubstantial claim

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

    Study design and conductnone given

    The study was approved by the IRB at King Saud University (Approval # 23/0155/IRB) and informed consent was obtained from all participants.

    “The Institutional Review Board (IRB) at King Saud University approved the study (Approval # 23/0155/IRB). Informed consent was obtained from all participants.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductreasoning

    The survey was piloted among 20 HCWs and refined based on feedback, resulting in a valid and reliable instrument.

    “As a result of their feedback, it was further refined to eliminate any ambiguities, ultimately resulting in a well-structured, valid, and reliable instrument for assessing HCWs’ perceptions of ChatGPT (Supplementary Materials).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    ChatGPT was frequently utilized to quickly generate educational materials and provide healthcare advice to patients and communities.

    “ChatGPT was frequently utilized to quickly generate educational materials and provide healthcare advice to patients and communities [22,23,24].”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [22] Alhasan, K., Al-Tawfiq, J., Aljamaan, F., Jamad, A., Al-Eyadhy, A., and Temsah, M.-H. (2023). Mitigating the Burden of Severe Pediatric Respiratory Viruses in the Post-COVID-19 Era: ChatGPT Insights and Recommendations. Cureus. source

      Not checked in the free run.

    • cites [23] Temsah, 2023, Reflection with ChatGPT about the excess death after the COVID-19 pandemic, New Microbes New Infect. source

      Not checked in the free run.

    • cites [24] Alhasan, K., Raina, R., Jamal, A., and Temsah, M.H. (2023). Combining human and AI could predict nephrologies future, but should be handled with care. Acta Paediatr. source

      Not checked in the free run.

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

    Premisecitation

    Previous studies have reported concerns about the accuracy and reliability of AI Chatbots in healthcare, labeled as "Artificial Hallucinations".

    “These findings are consistent with previous studies that have reported concerns about the accuracy and reliability of AI Chatbots in healthcare and were labeled as “Artificial Hallucinations” [26,27].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [26] Howard, 2023, ChatGPT and antimicrobial advice: The end of the consulting infection doctor?, Lancet Infect. Dis. source

      Not checked in the free run.

    • cites [27] Alkaissi, 2023, Artificial Hallucinations in ChatGPT: Implications in Scientific Writing, Cureus

      Not checked in the free run.

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

    Premisecitation

    The checklist emphasizes the need for algorithms and data to be auditable and accessible and for redressal processes to be fair and equally accessible.

    “The checklist emphasizes the need for algorithms and data to be auditable and accessible and for redressal processes to be fair and equally accessible [31].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [31] Parikh, 2023, Judge Juan Manuel Padilla Garcia, ChatGPT, and a controversial medicolegal milestone, Indian J. Med. Sci. source

      Not checked in the free run.

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

    Premisecitation

    The European Commission has outlined a checklist for trustworthy AI, which includes requirements for human oversight, robustness and safety, data privacy, process transparency, equitability, societal well-being, and accountability.

    “The European Commission has outlined a checklist for trustworthy AI, which includes requirements for human oversight, robustness and safety, data privacy, process transparency, equitability, societal well-being, and accountability [32].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [32] Commission, E. (2023, March 26). Ethics Guidelines for Trustworthy AI. Available online: https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai.

      Not checked in the free run.

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

    Premisecitation

    One study showed that physicians had a deeper level of specific training in ethics than nurses.

    “One study showed that physicians had a deeper level of specific training in ethics than nurses [30].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [30] Leuter, 2018, Ethical difficulties in healthcare: A comparison between physicians and nurses, Nurs. Ethics source

      Not checked in the free run.

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

    Interpretationnone given

    This is the first study that explored early HCWs' usage of ChatGPT.

    “To the best of our knowledge, this is the first study that explored early HCWs’ usage of ChatGPT.”
    Quote in the paperNot a substantial claim

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

    Premisecitation

    Prior to the ChatGPT era, several papers described adaptations of AI Chatbots by specific medical disciplines, with promising initial results.

    “Prior to the ChatGPT era, several papers described adaptations of AI Chatbots by specific medical disciplines, with promising initial results [35].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [35] Noble, 2022, Developing, Implementing, and Evaluating an Artificial Intelligence-Guided Mental Health Resource Navigation Chatbot for Health Care Workers and Their Families During and Following the COVID-19 Pandemic: Protocol for a Cross-sectional Study, JMIR Res. Protoc. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Study design and conductnone given

    The anonymity of the survey responses will ensure that participants feel comfortable providing honest and accurate responses.

    “Additionally, the anonymity of the survey responses will ensure that participants feel comfortable providing honest and accurate responses.”
    Quote in the paperNot a substantial claim

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

    Premisecitation

    A recent study found that transparency, accountability, and user-centered design are key factors that can improve the acceptance of AI Chatbots among healthcare professionals.

    “A recent study found that transparency, accountability, and user-centered design are key factors that can improve the acceptance of AI Chatbots among healthcare professionals [34].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [34] Borji, A. (2023). A categorical archive of chatgpt failures. arXiv. source

      Not checked in the free run.

  • May combine two claims.

    Study design and conductreasoning

    The study is subject to sampling bias, response bias, and recall bias.

    “As a cross-sectional survey study, this research is subject to some limitations, including sampling bias, response bias, and recall bias.”
    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

    Response bias may occur if participants provide socially desirable answers, misunderstand questions, or if unmeasured confounding factors influenced results.

    “Response bias may occur if participants provide answers that are socially desirable, if they misunderstand the questions, or if there were other unmeasured confounding factors that could have influenced the results.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    ChatGPT is seen as a potentially beneficial tool in healthcare settings, but concerns about its accuracy, reliability, and medicolegal implications persist.

    “While ChatGPT is seen as a potentially beneficial tool in healthcare settings, concerns about its accuracy, reliability, and medicolegal implications persist.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The survey responses accurately reflect the participants' true perceptions and experiences.

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

    Study design and conductnone given

    The study has a relatively large sample size of HCWs from different regions in Saudi Arabia.

    “Additionally, the study has a relatively large sample size of HCWs from different regions in Saudi Arabia.”
    Quote in the paperNot a substantial claim
21 of 21

  • The paper relies on this without support.

    At the time of the study, there was no available data to assess healthcare workers' experience with ChatGPT.

    “With ChatGPT’s recent emergence, there is no available data to assess healthcare workers’ (HCWs) experience, which is crucial information, given the potential consequences to healthcare.”

  • The paper relies on this without support.

    Current forms of ChatGPT and other Chatbots are unreliable.

    “Therefore, due to their unreliability, the current forms of ChatGPT and other Chatbots should not be used for diagnostic or treatment purposes without human expert oversight.”

  • The paper relies on this without support.

    The social media recruitment sample is representative of healthcare workers in Saudi Arabia.

    “This recruitment method was chosen to ensure broad geographic coverage and to include a diverse group of HCWs, considering the widespread use of these social media platforms among HCWs in Saudi Arabia [14].”

  • The paper relies on this without support.

    The survey responses accurately reflect the participants' true perceptions and experiences.

    “Additionally, the anonymity of the survey responses will ensure that participants feel comfortable providing honest and accurate responses.”

  • The paper relies on this without support.

    ChatGPT was launched only three months prior to the study.

    “ChatGPT was launched only three months prior to our study”
5 of 5

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?

  • Facilitators of trust include endorsement of the technology by experts and academically backed clinicians (including regulating bodies such as the government), evidence of output accuracy based on evaluation of AI, and positive opinions from trusted thought leaders in clinical fields.

    “Facilitators of trust included the endorsement of the technology by experts as well as academically backed clinicians, including regulating bodies such as the government; evidence of output accuracy based on the evaluation of AI; and positive opinions from trusted thought leaders in the respective clinical fields [42,58,60].”

    From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [60]

    Section 4.3, “Trust and Credibility of ChatGPT” (page not provided), says that evidence-based data on ChatGPT’s accuracy and reliability could enhance healthcare workers’ trust. The cited paper does not identify endorsement by experts, academically backed clinicians, or government bodies, or positive opinions from clinical thought leaders, as facilitators of trust. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Lack of regulation and policies from the government, including uncertainty around legal direction or law, is a barrier to the application of AI technologies.

    “A lack of regulation and policies from the government, including uncertainty around legal direction or law, was presented as a barrier to the application of AI technologies [37,45,57,60,77,78].”

    From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [60]

    Section 4.5 (page not provided) says healthcare workers perceived medicolegal implications as a significant barrier to using ChatGPT in healthcare and calls for clear guidelines and regulations. It does not say that a lack of government policies or uncertainty about legal direction or law was itself identified as the barrier; it also refers to existing local bylaws. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • In pharmaceutical research, ChatGPT can contribute by sifting through vast volumes of scientific literature, identifying potential drug candidates, and suggesting innovative research directions.

    “In the realm of pharmaceutical research, ChatGPT can contribute by sifting through vast volumes of scientific literature, identifying potential drug candidates, and suggesting innovative research directions.”

    From A Systematic Review and Meta-Analysis of Artificial Intelligence Tools in Medicine and Healthcare: Applications, Considerations, Limitations, Motivation and Challenges (2024) · cites this paper as [36]

  • ChatGPT can assist in drug discovery, research proposal writing, and summarising complex medical research, expediting the research process.

    “It can assist in drug discovery, research proposal writing, and summarising complex medical research, expediting the research process [11,24,34,35,36,37,38].”

    From A Systematic Review and Meta-Analysis of Artificial Intelligence Tools in Medicine and Healthcare: Applications, Considerations, Limitations, Motivation and Challenges (2024) · cites this paper as [36]

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.

  • Survey extended beyond the first three months after launch

    Minor

    The paper dates ChatGPT's launch to 30 November 2022, so the first three months ended by the end of February 2023. The survey continued through 6 March, contrary to the stated timeframe.

1 of 1

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Related

Authors
Temsah, M.-H., Aljamaan, F., Malki, K. H., Alhasan, K., Altamimi, I., Aljarbou, R., Bazuhair, F., Alsubaihin, A., Abdulmajeed, N., Alshahrani, F. S., Temsah, R., Alshahrani, T., Al-Eyadhy, L., Alkhateeb, S. M., Saddik, B., Halwani, R., Jamal, A., Al-Tawfiq, J. A., & Al-Eyadhy, A.
Affiliations
  • King Saud Medical City (SA)
  • King Saud University (SA)
  • King Faisal Specialist Hospital & Research Centre (SA)
  • Pediatric Department, King Saud University Medical City, King Saud University, Riyadh 11411, Saudi Arabia
  • Riyadh Armed Forces Hospital (SA)
  • Alfaisal University (SA)
  • Jordan University of Science and Technology (JO)
  • University of Sharjah (AE)
  • Specialty Internal Medicine and Quality Department, Johns Hopkins Aramco Healthcare, Dhahran 34465, Saudi Arabia
Corresponding author
Temsah, Mohamad-Hani
ORCID
Venue
Healthcare
Publisher
MDPI AG
Published
2023-06-21
Volume
11
Issue
13
Pages
1812
ISSN
2227-9032 (ISSN-L)
DOI
10.3390/healthcare11131812
PMC
PMC10340744
OpenAlex
W4381550225
Cited by
179
References
39
Citations by year
2026: 30 · 2025: 59 · 2024: 65 · 2023: 25
FWCI
7.13
Type
journal article
Language
en
Open access
Licence
Locations
  • Healthcare (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • IUScholarWorks (Indiana University) (submittedVersion, cc-by)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
ChatGPT, AI chatbots, healthcare workers, digital health, AI adoption barriers, perceived usefulness, clinical decision-making, AI credibility, medicolegal issues, Saudi Arabia

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