Evidence map›Paper›PMID 30710543›Full record

ReviewThe American journal of medicine2019

Artificial Intelligence Transforms the Future of Health Care.

Nariman Noorbakhsh-Sabet, Ramin Zand, Yanfei Zhang, Vida Abedi

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in The American journal of medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 197 papers, 11 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
197citing papers in PubMed, 11 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

197 citing papers in PubMed, 11 syntheses or guidelines pooled it.

  1. Pooled it
  2. Application of artificial intelligence in laryngeal lesions: a systematic review and meta-analysis.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025
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137 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Nariman Noorbakhsh-SabetSt. Jude Children's Research Hospital, Memphis, Tenn; University of Tennessee Health Science Center, Memphis.
Ramin ZandGeisinger Neuroscience Institute, Geisinger Health System, Danville, Penn; University of Tennessee Health Science Center, Memphis; Biocomplexity Institute, Virginia Tech, Blacksburg, Va.
Yanfei ZhangGenomic Medicine Institute, Geisinger Health System, Danville,Penn.
Vida AbediBiomedical and Translational Informatics Institute, Geisinger Health System, Danville, Penn; Biocomplexity Institute, Virginia Tech, Blacksburg, Va. Electronic address: vidaabedi@gmail.com.

Funding

Electronic Health Record Data and Predictive Analytic Methods for HFR56HL116832 · NHLBI · CALIFORNIA PACIFIC MED CTR RES INSTITUTE · PI YAN, XIAOWEI SHERRY · 2018 to 2018
$761k
NHLBI NIH HHS R56 HL116832
6 · The paper itself

Abstract

Life sciences researchers using artificial intelligence (AI) are under pressure to innovate faster than ever. Large, multilevel, and integrated data sets offer the promise of unlocking novel insights and accelerating breakthroughs. Although more data are available than ever, only a fraction is being curated, integrated, understood, and analyzed. AI focuses on how computers learn from data and mimic human thought processes. AI increases learning capacity and provides decision support system at scales that are transforming the future of health care. This article is a review of applications for machine learning in health care with a focus on clinical, translational, and public health applications with an overview of the important role of privacy, data sharing, and genetic information.

Indexed as

Artificial IntelligenceDecision Support Systems, ClinicalDelivery of Health CareDrug DiscoveryEpidemicsForecastingHumansMachine LearningTranslational Research, BiomedicalArtificial intelligence (AI)Integrated health care systemsMachine learningMedical informaticsPrecision medicine

Identifiers

PMID30710543
PMCPMC6669105

What OpenQuestion holds

Textmetadata
LicenceTDM
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.