Evidence map›Paper›PMID 34264433›Full record

ArticleObesity surgery2021

A Scoping Review of Artificial Intelligence and Machine Learning in Bariatric and Metabolic Surgery: Current Status and Future Perspectives.

Athanasios G Pantelis, Georgios K Stravodimos, Dimitris P Lapatsanis

Abstract readScoping Review
PubMed Publisher
In one paragraph

Article in Obesity surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Predicting readmission after bariatric surgery using machine learning.Surgery for obesity and related diseases : official journal of the American Society for Bariatric Surgery · 2023
    Article
  5. Review
  6. Article
  7. 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

3 authors.

Athanasios G Pantelis4th Department of Surgery, Bariatric and Metabolic Surgery Unit, Evaggelismos General Hospital of Athens, Ipsilantou 45-47, 10676, Athens, Greece. ath.pantelis@gmail.com.ORCID http://orcid.org/0000-0002-1788-2826
Georgios K Stravodimos4th Department of Surgery, Bariatric and Metabolic Surgery Unit, Evaggelismos General Hospital of Athens, Ipsilantou 45-47, 10676, Athens, Greece.
Dimitris P Lapatsanis4th Department of Surgery, Bariatric and Metabolic Surgery Unit, Evaggelismos General Hospital of Athens, Ipsilantou 45-47, 10676, Athens, Greece.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is a revolution in data analysis with emerging roles in various specialties and with various applications. The objective of this scoping review was to retrieve current literature on the fields of AI that have been applied to metabolic bariatric surgery (MBS) and to investigate potential applications of AI as a decision-making tool of the bariatric surgeon. Initial search yielded 3260 studies published from January 2000 until March 2021. After screening, 49 unique articles were included in the final analysis. Studies were grouped into categories, and the frequency of appearing algorithms, dataset types, and metrics were documented. The heterogeneity of current studies showed that meticulous validation, strict reporting systems, and reliable benchmarking are mandatory for ensuring the clinical validity of future research.

Indexed as

BariatricsBariatric SurgeryObesity, MorbidArtificial IntelligenceHumansMachine Learningartificial intelligencebig datadeep learningmachine learningmetabolic bariatric surgerypredictive models

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.