Evidence map›Paper›PMID 35106506›Full record

ReviewCell reports. Medicine2022

The inclusion of augmented intelligence in medicine: A framework for successful implementation.

George Bazoukis, Jennifer Hall, Joseph Loscalzo, Elliott Marshall Antman, Valentín Fuster, Antonis A Armoundas

Abstract readReview
In one paragraph

Review in Cell reports. Medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
70citing papers in PubMed, 2 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

70 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  18. Advances in laboratory medicine · 2025
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10 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

6 authors.

George BazoukisUniversity of Nicosia, Medical School, Nicosia, Cyprus.
Jennifer HallAmerican Heart Association, National Center, Dallas, TX.
Joseph LoscalzoDepartment of Medicine, Brigham and Women's Hospital, Boston, MA.
Elliott Marshall AntmanDepartment of Medicine, Brigham and Women's Hospital, Boston, MA.
Valentín FusterCardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York.
Antonis A ArmoundasCardiovascular Research Center, Massachusetts General Hospital, Boston, MA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) algorithms are being applied across a large spectrum of everyday life activities. The implementation of AI algorithms in clinical practice has been met with some skepticism and concern, mainly because of the uneasiness that stems, in part, from a lack of understanding of how AI operates, together with the role of physicians and patients in the decision-making process; uncertainties regarding the reliability of the data and the outcomes; as well as concerns regarding the transparency, accountability, liability, handling of personal data, and monitoring and system upgrades. In this viewpoint, we take these issues into consideration and offer an integrated regulatory framework to AI developers, clinicians, researchers, and regulators, aiming to facilitate the adoption of AI that rests within the FDA's pathway, in research, development, and clinical medicine.

Indexed as

Artificial IntelligenceMedicineAlgorithmsClinical Decision-MakingHumansPhysiciansReproducibility of Results

Identifiers

PMID35106506
PMCPMC8784713

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.