Evidence map›Paper›PMID 39133362›Full record

ReviewAbdominal radiology (New York)2025

AI-powered innovations in pancreatitis imaging: a comprehensive literature synthesis.

Sebastian Maletz, Yoga Balagurunathan, Kade Murphy, Les Folio, Ranjit Chima, Atif Zaheer, Harshna Vadvala

Abstract readReview
PubMed Publisher
In one paragraph

Review in Abdominal radiology (New York), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

7 authors.

Sebastian MaletzUniversity of South Florida Morsani College of Medicine, Tampa, USA.
Yoga BalagurunathanMoffitt Cancer Center, Tampa, USA.
Kade MurphyUniversity of South Florida Morsani College of Medicine, Tampa, USA.
Les FolioUniversity of South Florida Morsani College of Medicine, Tampa, USA.
Ranjit ChimaUniversity of South Florida Morsani College of Medicine, Tampa, USA.
Atif ZaheerJohns Hopkins Hospital, Baltimore, USA.
Harshna VadvalaUniversity of South Florida Morsani College of Medicine, Tampa, USA. drharshna@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early identification of pancreatitis remains a significant clinical diagnostic challenge that impacts patient outcomes. The evolution of quantitative imaging followed by deep learning models has shown great promise in the non-invasive diagnosis of pancreatitis and its complications. We provide an overview of advancements in diagnostic imaging and quantitative imaging methods along with the evolution of artificial intelligence (AI). In this article, we review the current and future states of methodology and limitations of AI in improving clinical support in the context of early detection and management of pancreatitis.

Indexed as

Artificial IntelligencePancreatitisDiagnostic ImagingEarly DiagnosisHumansImage Interpretation, Computer-AssistedArtificial intelligence (AI)Deep learning (DL)Machine learning (ML)Pancreatitis imaging

Identifiers

PMID39133362

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.