Evidence map›Paper›PMID 38624757›Full record

ReviewThe cardiothoracic surgeon2021

Requirement of artificial intelligence technology awareness for thoracic surgeons.

Anshuman Darbari, Krishan Kumar, Shubhankar Darbari, Prashant L Patil

Abstract readReview
In one paragraph

Review in The cardiothoracic surgeon, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
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.

Anshuman DarbariCTVS Department, AIIMS, Rishikesh, 249203 India.ORCID 0000-0002-4524-2359
Krishan KumarCSE Department, National Institute of Technology, Srinagar, Uttarakhand 246174 India.ORCID https://orcid.org/0000-0002-7068-6541
Shubhankar DarbariCSE, Technical University, Delft, Netherlands.ORCID https://orcid.org/0000-0003-3851-0033
Prashant L PatilCTVS Department, AIIMS, Rishikesh, 249203 India.ORCID https://orcid.org/0000-0002-3896-4636

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: We have recently witnessed incredible interest in computer-based, internet web-dependent mechanisms and artificial intelligence (AI)-dependent technique emergence in our day-to-day lives. In the recent era of COVID-19 pandemic, this nonhuman, machine-based technology has gained a lot of momentum. Main body of the abstract: The supercomputers and robotics with AI technology have shown the potential to equal or even surpass human experts' accuracy in some tasks in the future. Artificial intelligence (AI) is prompting massive data interweaving with elements from many digital sources such as medical imaging sorting, electronic health records, and transforming healthcare delivery. But in thoracic surgical and our counterpart pulmonary medical field, AI's main applications are still for interpretation of thoracic imaging, lung histopathological slide evaluation, physiological data interpretation, and biosignal testing only. The query arises whether AI-enabled technology-based or autonomous robots could ever do or provide better thoracic surgical procedures than current surgeons but it seems like an impossibility now. Short conclusion: This review article aims to provide information pertinent to the use of AI to thoracic surgical specialists. In this review article, we described AI and related terminologies, current utilisation, challenges, potential, and current need for awareness of this technology.

Indexed as

Artificial intelligenceDeep learningMachine learningRobotic surgeryThoracic cancersThoracic surgery

Identifiers

PMID38624757
PMCPMC8254051

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.