Ethics Education for Healthcare Professionals in the Era of ChatGPT and Other Large Language Models: Do We Still Need It?
Open accessAbstract
In this paper, we contend with whether we still need traditional ethics education as part of healthcare professional training given the abilities of chatGPT (generative pre-trained transformer) and other large language models (LLM). We reflect on common programmatic goals to assess the current strengths and limitations of LLMs in helping to build ethics competencies among future clinicians. Through an actual case analysis, we highlight areas in which chatGPT and other LLMs are conducive to common bioethics education goals. We also comment on where such technologies remain an imperfect substitute for human-led ethics teaching and learning. Finally, we conclude that the relative strengths of chatGPT warrant its consideration as a teaching and learning tool in ethics education in ways that account for current limitations and build in flexibility as the technology evolves.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
AI chatbots may lack the ability to comprehend human speech nuances and contextually deeper meanings.
“For example, AI chatbots may lack the ability to comprehend human speech nuances and contextually deeper meanings [30,34].”
From Consumer Responses to Generative AI Chatbots Versus Search Engines for Product Evaluation (2025) · cites this paper as [34]
The citation lists haven’t been fetched yet
References 36 · Cited by 66
- Authors
- Rahimzadeh, V., Kostick-Quenet, K., Blumenthal Barby, J., & McGuire, A. L.
- Affiliations
- Baylor College of Medicine (US)
- Baylor College of Medicine
- Corresponding author
- Rahimzadeh, Vasiliki
- Venue
- The American Journal of Bioethics
- Publisher
- Informa UK Limited
- Published
- 2023-07-24
- Volume
- 23
- Issue
- 10
- Pages
- 17-27
- ISSN
- 1526-5161 (ISSN-L)
- 1536-0075
- OpenAlex
- W4385190665
- Cited by
- 66
- References
- 36
- Citations by year
- 2026: 11 · 2025: 21 · 2024: 27 · 2023: 7
- FWCI
- 2.64
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- The American Journal of Bioethics (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Commons-TMC (Texas Medical Center) (submittedVersion)
- 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
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics and bioethics in healthcare
- Keywords
- ethics education, health professions education, ChatGPT, large language models, bioethics education, artificial intelligence in medical education, generative AI limitations
- MeSH terms
- Large Language Models, Generative Artificial Intelligence, Bioethics, Curriculum, Health Personnel, Humans, Language, Models, Educational
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