The DOI matches its Crossref record, with a small difference in title or year; 7 of the 7 references it cites were found in a registry or an index.
How Chatbots and Large Language Model Artificial Intelligence Systems Will Reshape Modern Medicine: Fountain of Creativity or Pandora’s Box?
Open accessAbstract
In an era of clinicians being burned out by electronic medical records and documentation burdens, we might all dream of having a personal scribe to draft progress notes, translate patient instructions, summarize the literature, complete insurance authorization paperwork, and respond to unending in-basket messages, as described in the Perspective in this issue of JAMA Internal Medicine. 1 This would have sounded like a fantasy just a few years ago, but the release of rapidly developing chatbots now demonstrates the potential of large language model artificial intelligence (AI) systems with surprisingly adept language manipulation and knowledge processing capabilities.The underlying foundation model technology rides atop the peak of inflated expectations, 2 reflecting a disruptive technology likely to change the way we work and live, even as we must be aware of substantial limitations.Good or bad, ready or not, Pandora's box has already been opened.One such large language model, ChatGPT, is the fastest-growing internet application in history with more than 100 million users.3 This has shifted access to sophisticated AI capabilities away from concentrated pockets of technical experts to the masses, where all types of otherwise unimaginable (and unintended) use cases are being discovered.To ensure that the adoption of such tools into health care practice is done effectively and responsibly, physicians must lean in to understand and drive this conversation.Large language models represent the underlying class of machine learning models trained in autocomplete tasks.Given the words "coronary artery," these models may predict the next word to be "disease," "bypass graft," or "calcification" based on statistical parameters learned from prior training data text on how often those words appear together.
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“The effective deployment of AI agents is hindered by several technical barriers. Efficiency remains a significant issue, as some tasks require extended processing times, impeding timely decision-making in critical medical scenarios.130”
From A foundational architecture for AI agents in healthcare (2025) · cites this paper as [130]
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Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000198 · Cited by 4,115
Large language models encode clinical knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2023) · Nature · doi:10.1038/s41586-023-06291-2 · Cited by 4,083
Large Language Models Encode Clinical Knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2212.13138 · Cited by 4,083
Training Language Models to Follow Instructions with Human Feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-2011 · Cited by 3,117
Ayers, J. W., Poliak, A., Dredze, M., Leas, E. C., Zhu, Z., Kelley, J. B., Faix, D. J., Goodman, A. M., Longhurst, C. A., Hogarth, M., et al. (2023) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2023.1838 · Cited by 2,613
Trustworthy Augmented Intelligence in Health Care
Crigger, E., Reinbold, K., Hanson, C., Kao, A., Blake, K., & Irons, M. (2022) · Journal of Medical Systems · doi:10.1007/s10916-021-01790-z · Cited by 132
Medicine in the Era of Artificial Intelligence
Brender, T. D. (2023) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2023.1832 · Cited by 23
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Research works printed but not linked: 1
- Chen, 2019, Artificial Intelligence in Health Care—The Hope, The Hype, The Promise, The Peril
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Items 101–102 of 102
The Long road to AI–physician partnership starts with learners: a map for AI in medical education
Patel, N., Kumar, A. D., Chi, J., Keet, K., Kugler, J., Hom, J., & Hosamani, P. (2026) · BMJ Digital Health & AI · doi:10.1136/bmjdh-2026-000056 · Cited by 1
Self-citationÖzdemir, Ç., Yeşil, B. M. B., & Özdemir, M. G. (2026) · Gazi Medical Journal · doi:10.12996/gmj.2026.4729
Metadata
- Authors
- Li, R., Kumar, A., & Chen, J. H.
- Affiliations
- Stanford Medicine (US)
- Venue
- JAMA Internal Medicine
- Publisher
- American Medical Association (AMA)
- Published
- 2023-06-01
- Volume
- 183
- Issue
- 6
- Pages
- 596
- ISSN
- 2168-6106 (ISSN-L)
- PMC
- PMC12049698
- OpenAlex
- W4367311208
- Cited by
- 104
- References
- 6
- Citations by year
- 2026: 11 · 2025: 30 · 2024: 34 · 2023: 29
- FWCI
- 4.15
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- JAMA Internal Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- American Heart Association (COVID-19)
- National Institutes of Health (COVID-19)
- National Institute on Drug Abuse (UG1DA015815)
- National Institute of Allergy and Infectious Diseases (R01AI178121)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- chatbots, large language models, artificial intelligence in healthcare, ChatGPT, clinical documentation burden, electronic health records, physician burnout, AI adoption in healthcare, clinical workflow automation
- MeSH terms
- Artificial Intelligence, Humans
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