Evidence map›Paper›PMID 33833482›Full record

ReviewWorld journal of gastroenterology2021

Artificial intelligence for early detection of pancreatic adenocarcinoma: The future is promising.

Antonio Mendoza Ladd, David L Diehl

Abstract readReview
In one paragraph

Review in World journal of gastroenterology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

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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

2 authors.

Antonio Mendoza LaddDepartment of Internal Medicine, Division of Gastroenterology, Texas Tech University Health Sciences Center El Paso, El Paso, TX 79905, United States. dr_ladd25@yahoo.com.
David L DiehlDepartment of Gastroenterology and Nutrition, Geisinger Medical Center, Danville, PA 17822, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is a worldwide public health concern. Despite extensive research efforts toward improving diagnosis and treatment, the 5-year survival rate at best is approximately 15%. This dismal figure can be attributed to a variety of factors including lack of adequate screening methods, late symptom onset, and treatment resistance. Pancreatic ductal adenocarcinoma remains a grim diagnosis with a high mortality rate and a significant psy-chological burden for patients and their families. In recent years artificial intelligence (AI) has permeated the medical field at an accelerated pace, bringing potential new tools that carry the promise of improving diagnosis and treatment of a variety of diseases. In this review we will summarize the landscape of AI in diagnosis and treatment of PDAC.

Indexed as

AdenocarcinomaCarcinoma, Pancreatic DuctalPancreatic NeoplasmsArtificial IntelligenceForecastingHumansArtificial intelligenceEarly diagnosisFuture perspectivesImproved performanceNeural networkPancreatic adenocarcinoma

Identifiers

PMID33833482
PMCPMC8015296

What OpenQuestion holds

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LicenceCC BY-NC
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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.