Evidence map›Paper›PMID 35058619›Full record

ReviewNature medicine2022

AI in health and medicine.

Pranav Rajpurkar, Emma Chen, Oishi Banerjee, Eric J Topol

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1,196 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1,196citing papers in PubMed, 6 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1,196 citing papers in PubMed, 6 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
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  4. Pooled it
  5. Pooled it
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  7. Review
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  15. SMART: structured, meaningful, auditable, responsible, and transparent documentation for clinical AI.Journal of the American Medical Informatics Association : JAMIA · 2026
    Article
  16. Article
  17. Article
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  19. Rethinking biomarkers in psychiatry: from etiological uncertainty to actionable prediction.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2026
    Article
  20. Article

1,136 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Pranav Rajpurkar *Department of Biomedical Informatics, Harvard University, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-8030-3727
Emma Chen *Department of Computer Science, Stanford University, Stanford, CA, USA.
Oishi Banerjee *Department of Computer Science, Stanford University, Stanford, CA, USA.
Eric J TopolScripps Translational Science Institute, San Diego, CA, USA. etopol@scripps.edu.ORCID http://orcid.org/0000-0002-1478-4729

Funding

Scripps Translational Science InstituteUL1TR002550 · NCATS · SCRIPPS RESEARCH INSTITUTE, THE · PI TOPOL, ERIC JEFFREY · 2018 to 2022
$28.9M
NCATS NIH HHS UL1 TR002550
6 · The paper itself

Abstract

Artificial intelligence (AI) is poised to broadly reshape medicine, potentially improving the experiences of both clinicians and patients. We discuss key findings from a 2-year weekly effort to track and share key developments in medical AI. We cover prospective studies and advances in medical image analysis, which have reduced the gap between research and deployment. We also address several promising avenues for novel medical AI research, including non-image data sources, unconventional problem formulations and human-AI collaboration. Finally, we consider serious technical and ethical challenges in issues spanning from data scarcity to racial bias. As these challenges are addressed, AI's potential may be realized, making healthcare more accurate, efficient and accessible for patients worldwide.

Indexed as

Artificial IntelligenceDelivery of Health CareMedicineAlgorithmsHumansProspective Studies

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.