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ChatGPT: friend or foe?
Open accessAbstract
You would have been hard-pressed to miss the storm surrounding ChatGPT (Chat Generative Pre-trained Transformer) over the past few months. News outlets and social media have been abuzz with reports on the chatbot developed by OpenAI. In response to a written prompt, ChatGPT can compose emails, write computer code, and even craft movie scripts. Researchers have also demonstrated its competency to pass medical licensing exams. But excitement has been matched by a swathe of ethical concerns that could—and perhaps should—limit its adoption
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“The moderate level of concern about potential negative impacts, as highlighted in our study, underscores the need for further exploration of the challenges associated with ChatGPT's implementation in healthcare and education settings [30].”
From Health profession students' perceptions of ChatGPT in healthcare and education: insights from a mixed-methods study (2025) · cites this paper as [30]
Experimental — accuracy not yet measuredIn the citing paper
“Advanced AI tools like ChatGPT are also poised to significantly impact the healthcare sector (Ali et al., 2023;Patel et al., 2023;Sallam, 2023).”
From Navigating the perils of artificial intelligence: a focused review on ChatGPT and responsible research and innovation (2024) · cites this paper as Patel et al., 2023
In the citing paper
“Furthermore, as the output of ChatGPT can be used to train future iterations of the model, any bias present in the data may persist without human intervention [5].”
From Ethical Considerations of Using ChatGPT in Health Care (2023) · cites this paper as [5]
In the citing paper
“Already ChatGPT, variant of the third GPT generation, has been trained to understand and generate language for conversation with users [12,13,14].”
From Optimizing Communication of Radiation Exposure in Medical Imaging, the Radiologist Challenge (2023) · cites this paper as [14]
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Metadata
- Authors
- Health, T. L. D.
- Corresponding author
- The Lancet Digital Health
- Venue
- The Lancet Digital Health
- Publisher
- Elsevier BV
- Published
- 2023-03
- Volume
- 5
- Issue
- 3
- Pages
- e102
- ISSN
- 2589-7500 (ISSN-L)
- OpenAlex
- W4319301633
- Cited by
- 257
- References
- 0
- Citations by year
- 2026: 15 · 2025: 43 · 2024: 82 · 2023: 116
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- The Lancet Digital Health (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- ChatGPT, large language models, chatbots, AI ethics, medical licensing examinations, AI-generated medical content, OpenAI, generative AI adoption
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 ›
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