ChatGPT and global public health: Applications, challenges, ethical considerations and mitigation strategies
Open accessAbstract
The advancement of deep learning and artificial intelligence has resulted in the development of state-of-the-art language models, such as ChatGPT. This technology can analyze large amounts of data, identify patterns, and assist in the analysis and understanding of risk factors for diseases. Despite its potential, the applications, challenges, and ethical considerations have not been yet fully explored in global health research. This paper examines the applications of ChatGPT in global health research, assesses the challenges in its use, and proposes mitigation strategies. Additionally, it describes the ethical considerations around the use of ChatGPT in global health research and suggests potential avenues for addressing these issues. This paper summarizes that it is crucial to understand the capabilities and limitations of this technology in order to fully realize its potential and ensure its responsible integration into global health research.
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Data diversification techniques such as augmenting underrepresented groups, counter-bias pairing, and re-balancing skewed corpora have proven effective at reducing differential performance.
“Data diversification techniques-such as augmenting underrepresented groups, counter-bias pairing, and re-balancing skewed corpora-have proven effective at reducing differential performance (Parray et al., 2023).”
From Trustworthy Medical Question Answering: An Evaluation-Centric Survey (2025) · cites this paper as Parray et al., 2023
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Chatting and cheating: Ensuring academic integrity in the era of ChatGPT
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024) · Innovations in Education and Teaching International · doi:10.1080/14703297.2023.2190148 · Cited by 3,087 · see citing works
Chatting and Cheating. Ensuring academic integrity in the era of ChatGPT
Cotton, D., Cotton, P., & Shipway, J. R. (2023) · doi:10.35542/osf.io/mrz8h · Cited by 3,079 · see citing works
Scalable and accurate deep learning with electronic health records
Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0029-1 · Cited by 2,648 · see citing works
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V., & Kalai, A. (2016) · arXiv · doi:10.48550/arxiv.1607.06520 · Cited by 1,615 · see citing works
ChatGPT 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
ChatGPT: The End of Online Exam Integrity?
Susnjak, T. (2022) · arXiv · doi:10.48550/arxiv.2212.09292 · Cited by 370 · see citing works
ChatGPT: Evolution or revolution?
Gordijn, B., & Have, H. T. (2023) · Medicine, Health Care and Philosophy · doi:10.1007/s11019-023-10136-0 · Cited by 317 · see citing works
Alshater, M. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4312358 · Cited by 304 · see citing works
Can ChatGPT draft a research article? An example of population-level vaccine effectiveness analysis
Macdonald, C., Adeloye, D., Sheikh, A., & Rudan, I. (2023) · Journal of Global Health · doi:10.7189/jogh.13.01003 · Cited by 243 · see citing works
DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature
Mitchell, E., Lee, Y., Khazatsky, A., Manning, C. D., & Finn, C. (2023) · arXiv · doi:10.48550/arxiv.2301.11305 · Cited by 151 · see citing works
Language models are an effective representation learning technique for electronic health record data
Steinberg, E., Jung, K., Fries, J. A., Corbin, C. K., Pfohl, S. R., & Shah, N. H. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103637 · Cited by 132 · see citing works
Ravaut, M., Sadeghi, H., Leung, K. K., Volkovs, M., Kornas, K., Harish, V., Watson, T., Lewis, G. F., Weisman, A., Poutanen, T., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00394-8 · Cited by 121 · see citing works
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Ullah, E., Parwani, A., Baig, M. M., & Singh, R. (2024) · Diagnostic Pathology · doi:10.1186/s13000-024-01464-7 · Cited by 281 · see citing works
Large language models streamline automated machine learning for clinical studies
Tayebi Arasteh, S., Han, T., Lotfinia, M., Kuhl, C., Kather, J. N., Truhn, D., & Nebelung, S. (2024) · Nature Communications · doi:10.1038/s41467-024-45879-8 · Cited by 114 · see citing works
A scoping review of ChatGPT's role in healthcare education and research
Shorey, S., Mattar, C., Pereira, T. L.-B., & Choolani, M. (2024) · Nurse Education Today · doi:10.1016/j.nedt.2024.106121 · Cited by 96 · see citing works
Sahu, P. K., Benjamin, L. A., Singh Aswal, G., & Williams-Persad, A. (2023) · Postgraduate Medical Journal · doi:10.1093/postmj/qgad090 · Cited by 52 · see citing works
Trustworthy Medical Question Answering: An Evaluation-Centric Survey
Wang, Y., Wang, B., Mercer, R. E., Rudzicz, F., Roy, S. S., Ren, P., Chen, Z., & Wang, X. (2025) · arXiv · doi:10.48550/arxiv.2506.03659
- Authors
- Parray, A. A., Inam, Z. M., Ramonfaur, D., Haider, S. S., Mistry, S. K., & Pandya, A. K.
- Affiliations
- Johns Hopkins University (US)
- George Washington University (US)
- The University of Sydney (AU)
- Corresponding author
- Parray, Ateeb Ahmad
- Venue
- Global Transitions
- Publisher
- Elsevier BV
- Published
- 2023
- Volume
- 5
- Pages
- 50-54
- ISSN
- 2589-7918 (ISSN-L)
- OpenAlex
- W4377861964
- Cited by
- 60
- References
- 12
- Citations by year
- 2026: 3 · 2025: 17 · 2024: 28 · 2023: 12
- FWCI
- 2.23
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Global Transitions (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 › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- ChatGPT, large language models, global health research, artificial intelligence in healthcare, ethical considerations, mitigation measures, disease risk factors, responsible AI use, technology limitations
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