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, including efficiency issues, quality of outputs, context length limitations, unexpected behaviors, and network reliability.
“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]
The Abstract discusses fabricated or inaccurate chatbot responses, but it does not address slow processing or delayed decisions in critical medical scenarios. A search of our copy for efficiency, processing time, and latency found no passage supporting the claim attached to this citation. (LLM, conf=0.76)
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- 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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