Let’s Have a Chat! A Conversation with ChatGPT: Technology, Applications, and Limitations
Open accessAbstract
The advent of artificial intelligence-empowered chatbots capable of constructing human-like sentences and articulating cohesive essays has captivated global interest. This paper provides a historical perspective on chatbots, focusing on the technology underpinning the Chat Generative Pre-trained Transformer, better known as ChatGPT. We underscore the potential utility of ChatGPT across a multitude of fields, including healthcare, education, and research. To the best of our knowledge, this is the first review that not only highlights the applications of ChatGPT in multiple domains but also analyzes its performance on examinations across various disciplines. Despite its promising capabilities, ChatGPT raises numerous ethical and privacy concerns that are meticulously explored in this paper. Acknowledging the current limitations of ChatGPT is crucial in understanding its potential for growth. We also ask ChatGPT to provide its point of view and present its responses to several questions we attempt to answer. Received: 5 April 2023 | Revised: 23 May 2023 | Accepted: 29 May 2023 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study.
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?
The need to rethink and revise current assessment tools in health care education comes in light of ChatGPT's ability to pass reputable exams (e.g., USMLE) and the possibility of ChatGPT misuse resulting in academic dishonesty.
“The need to rethink and revise the current assessment tools in health care education comes in light of ChatGPT’s ability to pass reputable exams (e.g., USMLE) and possibility of ChatGPT misuse, which would result in academic dishonesty [24,34,58,59,62,76,85,86,87].”
From ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns (2023) · cites this paper as [85]
The citation lists haven’t been fetched yet
References 69 · Cited by 167
- Authors
- Shahriar, S., & Hayawi, K.
- Affiliations
- University of Guelph (CA)
- Zayed University (AE)
- Venue
- Artificial Intelligence and Applications
- Publisher
- Bon View Publishing Pte
- Published
- 2023-06-02
- Volume
- 2
- Issue
- 1
- Pages
- 11-20
- ISSN
- 2811-0854 (ISSN-L)
- OpenAlex
- W4379410101
- Cited by
- 167
- References
- 69
- Citations by year
- 2026: 8 · 2025: 29 · 2024: 69 · 2023: 61
- FWCI
- 15.74
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Artificial Intelligence and Applications (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- ChatGPT, large language models, chatbots, generative AI, history of chatbots, medical applications, educational software, research applications, ethical considerations, privacy concerns, AI limitations, AI self-assessment
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