Evidence map›Paper›PMID 32989966›Full record

ReviewMinerva cardioangiologica2020

Artificial intelligence in cardiothoracic surgery.

Roger D Dias, Julie A Shah, Marco A Zenati

Open access · greenAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Minerva cardioangiologica, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 3 pooled it
2.8field-weighted citation impact, top 7% of its field
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

25 citing papers in PubMed, 3 syntheses or guidelines pooled it, 34 citations in OpenAlex.

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  7. Can artificial intelligence models predict hospital stays following non-small cell lung carcinoma surgery?Kardiochirurgia i torakochirurgia polska = Polish journal of cardio-thoracic surgery · 2025
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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

3 authors at 3 institutions in 1 country.

Roger D DiasSTRATUS Center for Medical Simulation, Brigham Health, Boston, MA, USA - rdias@bwh.harvard.edu.
Julie A ShahLaboratory of Computer Science and Artificial Intelligence, Massachusetts Institute of Technology, Cambridge, MA, USA.
Marco A ZenatiLaboratory of Medical Robotics and Computer Assisted Surgery (MRCAS), Division of Cardiothoracic Surgery, VA Boston Healthcare System, Boston, MA, USA.
Harvard University · USMassachusetts Institute of Technology · USVA Boston Healthcare System · US

Funding

Developing Standardized Intraoperative Process Models to Enhance Surgical SafetyR01HL126896 · NHLBI · HARVARD MEDICAL SCHOOL · PI ZENATI, MARCO A. · 2016 to 2023
$3.7M
NHLBI NIH HHS R01 HL126896
6 · The paper itself

Abstract

The tremendous and rapid technological advances that humans have achieved in the last decade have definitely impacted how surgical tasks are performed in the operating room (OR). As a high-tech work environment, the contemporary OR has incorporated novel computational systems into the clinical workflow, aiming to optimize processes and support the surgical team. Artificial intelligence (AI) is increasingly important for surgical decision making to help address diverse sources of information, such as patient risk factors, anatomy, disease natural history, patient values and cost, and assist surgeons and patients to make better predictions regarding the consequences of surgical decisions. In this review, we discuss the current initiatives that are using AI in cardiothoracic surgery and surgical care in general. We also address the future of AI and how high-tech ORs will leverage human-machine teaming to optimize performance and enhance patient safety.

Indexed as

Artificial IntelligenceCardiac Surgical ProceduresThoracic Surgical ProceduresHumans

Identifiers

PMID32989966
PMCPMC7959017
OpenAlexW3091364893

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.