Cited by 303Artificial Intelligence in Healthcare and EducationArtificial Intelligence in HealthcareArtificial Intelligence Applications

Artificial Intelligence in Medicine

Open access
Beam, Andrew L., Drazen, Jeffrey M., Kohane, Isaac S., Leong, Tze-Yun, Manrai, Arjun K., Rubin, Eric J.·2023·New England Journal of Medicine

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1056/nejme2206291

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • AI use in healthcare has steadily expanded in diagnostics, decision support, administration, and population health.

    “With the rise of artificial intelligence (AI), its use in healthcare, covering diagnostics, decision support, administration, and population health, has steadily expanded [1].”

    From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [1]

    The “Artificial Intelligence in Medicine” section (page unavailable) describes increasing AI use in image interpretation, medical notes, insurance claims, and analysis of large clinical databases. These examples support expanding use across several healthcare functions, but the copy does not specifically establish use in population health or a steady rate of expansion. (LLM, conf=0.83)

    Experimental — accuracy not yet measured
  • Traditional machine learning models have supported tumor classification, survival prediction, and risk stratification using structured clinical or genomic data.

    “traditional ML models, such as support vector machines (SVM), decision trees, and random forests, have supported tumor classification, survival prediction, and risk stratification using structured clinical or genomic data [6,12,13,14].”

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Beam, A. L., Drazen, J. M., Kohane, I. S., Leong, T.-Y., Manrai, A. K., & Rubin, E. J.
Affiliations
Harvard University (US)
Venue
New England Journal of Medicine
Published
2023-03-30
Volume
388
Issue
13
Pages
1220-1221
ISSN
  • 0028-4793 (ISSN-L)
  • 1533-4406
OpenAlex
W4361284227
Cited by
303
References
3
Citations by year
2026: 42 · 2025: 81 · 2024: 113 · 2023: 60 · 2022: 3 · 2021: 1
Type
journal article
Language
en
Open access
Locations
  • New England Journal of Medicine (publishedVersion)
  • PubMed (publishedVersion)
Funders
National Heart, Lung, and Blood Institute (K01HL141771)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords
artificial intelligence, artificial intelligence in healthcare, clinical decision support, health care
MeSH terms
Artificial Intelligence, Humans, Medicine

About these checks

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.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review