Evaluating the understanding of the ethical and moral challenges of Big Data and AI among Jordanian medical students, physicians in training, and senior practitioners: a cross-sectional study
Open accessAbstract
Aims: To examine the understanding of the ethical dilemmas associated with Big Data and artificial intelligence (AI) among Jordanian medical students, physicians in training, and senior practitioners. Methods: We implemented a literature-validated questionnaire to examine the knowledge, attitudes, and practices of the target population during the period between April and August 2023. Themes of ethical debate included privacy breaches, consent, ownership, augmented biases, epistemology, and accountability. Participants’ responses were showcased using descriptive statistics and compared between groups using t-test or ANOVA. Results: We included 466 participants. The greater majority of respondents were interns and residents (50.2%), followed by medical students (38.0%). Most participants were affiliated with university institutions (62.4%). In terms of privacy, participants acknowledged that Big Data and AI were susceptible to privacy breaches (39.3%); however, 59.0% found such breaches justifiable under certain conditions. For ethical debacles involving informed consent, 41.6% and 44.6% were aware that obtaining informed consent posed an ethical limitation in Big Data and AI applications and denounced the concept of “broad consent”, respectively. In terms of ownership, 49.6% acknowledged that data cannot be owned yet accepted that institutions could hold a quasi-control of such data (59.0%). Less than 50% of participants were aware of Big Data and AI’s abilities to augment or create new biases in healthcare. Furthermore, participants agreed that researchers, institutions, and legislative bodies were responsible for ensuring the ethical implementation of Big Data and AI. Finally, while demonstrating limited experience with using such technology, participants generally had positive views of the role of Big Data and AI in complementing healthcare. Conclusion: Jordanian medical students, physicians in training and senior practitioners have limited awareness of the ethical risks associated with Big Data and AI. Institutions are responsible for raising awareness, especially with the upsurge of such technology.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Fifteen publications focused explicitly on ethical considerations concerning the use of AI in medical education.
“15 publications focused explicitly on this topic [7, 38, 84, 97, 186, 190–199].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [191]
The Introduction (page unavailable) says this study examines medical students’ and healthcare workers’ understanding of ethical challenges associated with AI in medical practice. It therefore fits the citation list as a publication about AI ethics involving medical students, but it does not explicitly focus on the use of AI in medical education. As one cited publication, it also cannot establish the citing paper’s count of fifteen. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 34 · Cited by 21
Research works printed but not linked: 2
- [18]Imran N, Haider II, Jawaid M, Mazhar N. Health ethics education: knowledge, attitudes and practice of healthcare ethics among interns and residents in Pakistan. J Postgrad Med Inst. 2014;28:383–9.
- [38]Frické M. Big data and its epistemology. J Am Soc Inf Sci. 2015;66:651–61.
- Authors
- Al-Ani, A., Rayyan, A., Maswadeh, A., Sultan, H., Alhammouri, A., Asfour, H., Alrawajih, T., Al Sharie, S., Al Karmi, F., Al-Azzam, A. M., Mansour, A., & Al-Hussaini, M.
- Affiliations
- King Hussein Cancer Center (JO)
- University of Jordan (JO)
- Corresponding author
- Al-Hussaini, Maysa
- ORCID
- Venue
- BMC Medical Ethics
- Published
- 2024-02-17
- Volume
- 25
- Issue
- 1
- Pages
- 18-18
- ISSN
- 1472-6939 (ISSN-L)
- PMC
- PMC10873950
- OpenAlex
- W4391898286
- Cited by
- 21
- References
- 34
- Citations by year
- 2026: 9 · 2025: 8 · 2024: 4
- FWCI
- 1.95
- Type
- journal article
- Language
- en
- Update notices
- Open access
- gold
- doi.org
- Locations
- BMC Medical Ethics (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
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics and bioethics in healthcare
- Keywords
- artificial intelligence in healthcare, big data, medical ethics, moral consciousness, medical students, physicians in training, privacy violations, informed consent, broad consent, data ownership, algorithmic bias, accountability, epistemology, Jordan
- MeSH terms
- Big Data, Artificial Intelligence, Cross-Sectional Studies, Humans, Jordan, Morals, Physicians, Students, Medical
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review