Cited by 104Artificial Intelligence in Healthcare and EducationElectronic Health Records SystemsMachine Learning in Healthcare

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How Chatbots and Large Language Model Artificial Intelligence Systems Will Reshape Modern Medicine: Fountain of Creativity or Pandora’s Box?

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Li, Ron, Kumar, Andre, Chen, Jonathan H.·2023·JAMA Internal Medicine

DOI 10.1001/jamainternmed.2023.1835PMC PMC12049698

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Abstract

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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Authors: Li, R., Kumar, A., & Chen, J. H.
Affiliations: Stanford Medicine (US)
ORCID: Li, Ron
Kumar, Andre
Chen, Jonathan H.
Venue: JAMA Internal Medicine
Publisher: American Medical Association (AMA)
Published: 2023-06-01
Volume: 183
Issue: 6
Pages: 596
ISSN: 2168-6106 (ISSN-L)
DOI: 10.1001/jamainternmed.2023.1835
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)
Google
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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How Chatbots and Large Language Model Artificial Intelligence Systems Will Reshape Modern Medicine: Fountain of Creativity or Pandora’s Box?

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