This new conversational AI model can be your friend, philosopher, and guide ... and even your worst enemy
Open accessAbstract
We explore the recently released ChatGPT model, one of the most powerful conversational AI models that has ever been developed. This opinion provides a perspective on its strengths and weaknesses and a call to action for the AI community (including academic researchers and industry) to work together on preventing potential misuse of such powerful AI models in our everyday lives.
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In health care practice and academic writing, factual inaccuracies, ethical issues, and the fear of misuse including the spread of misinformation should be considered.
“In health care practice and academic writing, factual inaccuracies, ethical issues, and the fear of misuse including the spread of misinformation should be considered [14,15,16].”
From ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns (2023) · cites this paper as [15]
The ‘Our experimental interactions with the ChatGPT’ section (page not provided) warns of factual errors and discusses plagiarism in academic writing. The ‘Call to action’ section (page not provided) raises ethical concerns, potential misuse, and difficulty distinguishing fact from fiction. Our copy does not address these risks specifically in health care practice or explicitly discuss the spread of misinformation. (LLM, conf=0.84)
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Cites
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208 · see citing works
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Research works printed but not linked: 1
- [2]Brown T. Mann B. Ryder N. Subbiah M. Kaplan J.D. Dhariwal P. Neelakantan A. Shyam P. Sastry G. Askell A. Language models are few-shot learners Larochelle H. Ranzato M. Hadsell R. Balcan M. Lin H. Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS 2020) 2020 1877 1901 https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf
Cited by
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Sallam, M. (2023) · Healthcare · doi:10.3390/healthcare11060887 · Cited by 3,126 · see citing works
Dave, T., Athaluri, S. A., & Singh, S. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1169595 · Cited by 1,204 · see citing works
Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023) · The International Journal of Management Education · doi:10.1016/j.ijme.2023.100790 · Cited by 1,261 · see citing works
ChatGPT in education: Strategies for responsible implementation
Halaweh, M. (2023) · Contemporary Educational Technology · doi:10.30935/cedtech/13036 · Cited by 607 · see citing works
“Issues of plagiarism detection in write-ups generated by ChatGPT have been raised, as well as how to distinguish between fact and fiction text generated (Chatterjee & Dethlefs, 2023; Khalil & Er, 2023).”
BackgroundChatGPT in healthcare: A taxonomy and systematic review
Li, J., Dada, A., Puladi, B., Kleesiek, J., & Egger, J. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108013 · Cited by 452 · see citing works
Students' perceptions of using ChatGPT in a physics class as a virtual tutor
Ding, L., Li, T., Jiang, S., & Gapud, A. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00434-1 · Cited by 208 · see citing works
Ansari, A. N., Ahmad, S., & Bhutta, S. M. (2024) · Education and Information Technologies · doi:10.1007/s10639-023-12223-4 · Cited by 198 · see citing works
Deiana, G., Dettori, M., Arghittu, A., Azara, A., Gabutti, G., & Castiglia, P. (2023) · Vaccines · doi:10.3390/vaccines11071217 · Cited by 168 · see citing works
Learning with ChatGPT 3.5 as a more knowledgeable other: an autoethnographic study
Stojanov, A. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00404-7 · Cited by 169 · see citing works
Future applications of generative large language models: a data-driven case study on ChatGPT
Chiarello, F., Giordano, V., Spada, I., Barandoni, S., & Fantoni, G. (2024) · Technovation · doi:10.1016/j.technovation.2024.103002 · Cited by 164 · see citing works
Jeyaraman, M., Ramasubramanian, S., Balaji, S., Jeyaraman, N., Nallakumarasamy, A., & Sharma, S. (2023) · World Journal of Methodology · doi:10.5662/wjm.v13.i4.170 · Cited by 125 · see citing works
Mastering knowledge: the impact of generative AI on student learning outcomes
Pallant, J. L., Blijlevens, J., Campbell, A., & Jopp, R. (2026) · Studies in Higher Education · doi:10.1080/03075079.2025.2487570 · Cited by 94 · see citing works
Nelson, A. S., Santamaría, P. V., Javens, J. S., & Ricaurte, M. (2025) · Education Sciences · doi:10.3390/educsci15050611 · Cited by 79 · see citing works
ChatGPT in Ukrainian Education: Problems and Prospects
Fiialka, S., Kornieva, Z., & Honcharuk, T. (2023) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v18i17.42215 · Cited by 45 · see citing works
Suleiman, A., von Wedel, D., Munoz-Acuna, R., Redaelli, S., Santarisi, A., Seibold, E.-L., Ratajczak, N., Kato, S., Said, N., Sundar, E., et al. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108313 · Cited by 27 · see citing works
Naing, S. Z. S., & Udomwong, P. (2024) · Data Intelligence · doi:10.1162/dint_a_00250 · Cited by 15 · see citing works
ChatGPT -Boon or Bane: A Study from Students Perspective
Wagholikar, S., Chandani, A., Atiq, R., Pathak, M., & Wagholikar, O. (2023) · 2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) · doi:10.1109/incacct57535.2023.10141820 · Cited by 13 · see citing works
ChatGPT and its application in the field of mental health.
Bhattacharyya, R., Chakraborty, K., & Neogi, R. (2023) · Journal of SAARC Psychiatric Federation · doi:10.4103/jspf.jspf_9_23 · Cited by 12 · see citing works
ChatGPT and the Future of Writing about Writing
De Matas, J. (2023) · Double Helix: A Journal of Critical Thinking and Writing · doi:10.37514/dbh-j.2023.11.1.09 · Cited by 4 · see citing works
- Authors
- Chatterjee, J., & Dethlefs, N.
- Affiliations
- University of Hull (GB)
- Corresponding author
- Chatterjee, Joyjit
- Venue
- Patterns
- Publisher
- Elsevier BV
- Published
- 2023-01
- Volume
- 4
- Issue
- 1
- Pages
- 100676
- ISSN
- 2666-3899 (ISSN-L)
- PMC
- PMC9868650
- OpenAlex
- W4315874291
- Cited by
- 263
- References
- 1
- Citations by year
- 2026: 28 · 2025: 63 · 2024: 102 · 2023: 69 · 2022: 1
- FWCI
- 65.56
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Patterns (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Repository@Hull (Worktribe) (University of Hull) (publishedVersion, other-oa)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- ChatGPT, conversational AI, large language models, AI misuse prevention, AI ethics, AI safety, human-AI interaction
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