Cited by 11Artificial Intelligence in Healthcare and EducationArtificial Intelligence ApplicationsAI in cancer detection

A New Era in Surgical Research: The Evolving Role of Artificial Intelligence

Watanabe, Akie, Wiseman, Sam M.·2023·The American Journal of Surgery

Integrity signals

Registry

Crossref · 10.1016/j.amjsurg.2023.06.040

Cites

10 of 10 references verified0 citation errors · 0 not checkable

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  • LLMs can process and summarize vast amounts of medical literature quickly.

    “LLMs can process and summarize vast amounts of medical literature quickly (Watanabe and Wiseman 2023), a task often done by research assistants.”

    From Large language models (LLMs): survey, technical frameworks, and future challenges (2024) · cites this paper as Watanabe and Wiseman 2023

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Authors
Watanabe, A., & Wiseman, S. M.
Affiliations
University of British Columbia (CA)
Corresponding author
Wiseman, Sam M.
Venue
The American Journal of Surgery
Publisher
Elsevier BV
Published
2023-12
Volume
226
Issue
6
Pages
923-925
ISSN
0002-9610 (ISSN-L)
OpenAlex
W4382765980
Cited by
11
References
10
Citations by year
2026: 2 · 2025: 2 · 2024: 5 · 2023: 2
Type
journal article
Language
en
Open access
closed
Locations
  • The American Journal of Surgery (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Keywords
artificial intelligence, surgical research, machine learning, natural language processing, artificial neural networks, computer vision, image recognition, expert systems, medical diagnostics, radiology, pathology
MeSH terms
Artificial Intelligence, Humans, General Surgery, Biomedical Research

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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