The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
The promise of large language models in health care
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“Due to their training on a vast array of internet content, LLMs may inadvertently incorporate biases (Arora and Arora 2023) (e.g., gender bias, racial bias, geopolitical biases, religious bias, nationality bias, sexual orientation bias, and age bias, etc.) across the web, posing severe threats in sensitive fields (Korngiebel and Mooney 2021).”
From Large language models in medical and healthcare fields: applications, advances, and challenges (2024) · cites this paper as Arora and Arora 2023
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A large language model for electronic health records
Yang, X., Chen, A., PourNejatian, N., Shin, H. C., Smith, K. E., Parisien, C., Compas, C., Martin, C., Costa, A. B., Flores, M. G., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00742-2 · Cited by 847
Artificial Intelligence in mental health and the biases of language based models
Straw, I., & Callison-Burch, C. (2020) · PLOS ONE · doi:10.1371/journal.pone.0240376 · Cited by 164
Arora, A., & Arora, A. (2022) · Future Healthcare Journal · doi:10.7861/fhj.2022-0013 · Cited by 161
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Large language models in medicine
Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., & Ting, D. S. W. (2023) · Nature Medicine · doi:10.1038/s41591-023-02448-8 · Cited by 3,944
Li, H., Zhang, R., Lee, Y.-C., Kraut, R. E., & Mohr, D. C. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00979-5 · Cited by 598
“Moreover, the rapid progress in generative AI, such as Large Language Models (LLMs), necessitates an exploration of this technology ’ s potential and pitfalls, amidst uncertainties associated with its deployment in mental health care 16 .”
BackgroundChatGPT for healthcare services: An emerging stage for an innovative perspective
Javaid, M., Haleem, A., & Singh, R. P. (2023) · BenchCouncil Transactions on Benchmarks, Standards and Evaluations · doi:10.1016/j.tbench.2023.100105 · Cited by 429
The application of large language models in medicine: A scoping review
Meng, X., Yan, X., Zhang, K., Liu, D., Cui, X., Yang, Y., Zhang, M., Cao, C., Wang, J., Wang, X., et al. (2024) · iScience · doi:10.1016/j.isci.2024.109713 · Cited by 414
Generative AI in medical practice: in-depth exploration of privacy and security challenges
Chen, Y., & Esmaeilzadeh, P. (2024) · Journal of Medical Internet Research · doi:10.2196/53008 · Cited by 353
“LLMs such as GPT-4 (OpenAI) are trained on massive text data and can generate synthetic natural language text, code, and so on [10].”
Metadata
- Authors
- Arora, A., & Arora, A.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Arora, Anmol
- ORCID
- Arora, Anmol
- Venue
- The Lancet
- Publisher
- Elsevier BV
- Published
- 2023-02
- Volume
- 401
- Issue
- 10377
- Pages
- 641
- ISSN
- 0099-5355 (ISSN-L)
- 0140-6736
- OpenAlex
- W4321606060
- Cited by
- 153
- References
- 3
- Citations by year
- 2026: 23 · 2025: 49 · 2024: 43 · 2023: 37
- FWCI
- 6.07
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The Lancet (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- large language models, generative AI, health care, artificial general intelligence, ChatGPT, GPT-3, BERT, clinical applications of AI
- MeSH terms
- Delivery of Health Care, Health Facilities, Humans, Language
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