Evidence map›Paper›PMID 42122053›Full record

ReviewDiagnostics (Basel, Switzerland)2026

Management and Prediction of Acute Pancreatitis Severity Using AI: A Surgical Perspective.

Ioana Dumitrascu, Narcis Octavian Zarnescu, Giovanni Marchegiani, Alexandru Ilie, Eugenia Claudia Zarnescu, Radu Virgil Costea

Abstract readReview
In one paragraph

Review in Diagnostics (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Ioana DumitrascuDepartment of General Surgery, "Carol Davila" University of Medicine and Pharmacy, 050474 Bucharest, Romania.ORCID 0000-0002-6454-1725
Narcis Octavian ZarnescuDepartment of General Surgery, "Carol Davila" University of Medicine and Pharmacy, 050474 Bucharest, Romania.ORCID 0000-0003-3973-6309
Giovanni MarchegianiHepato Pancreato Biliary and Liver Transplant Surgery, Department of Surgery Oncology and Gastroenterology (DiSCOG), University of Padua, 35128 Padua, Italy.
Alexandru IlieSecond Department of Surgery, University Emergency Hospital Bucharest, 050098 Bucharest, Romania.
Eugenia Claudia ZarnescuDepartment of General Surgery, "Carol Davila" University of Medicine and Pharmacy, 050474 Bucharest, Romania.
Radu Virgil CosteaDepartment of General Surgery, "Carol Davila" University of Medicine and Pharmacy, 050474 Bucharest, Romania.ORCID 0000-0003-0509-693X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Acute pancreatitis is a common inflammatory digestive disease with an unpredictable clinical course, ranging from self-limited forms to severe forms, associated with complications and increased mortality. Early identification of patients at risk of severe disease is particularly important from a surgical perspective, as it has a significant impact on subsequent management. Traditional severity scores, such as APACHE (Acute Physiology And Chronic Health Evaluation) II and BISAP (Bedside Index for Severity in Acute Pancreatitis), remain widely used, but their rigid structure and delayed applicability may limit initial risk assessment. In this review we highlight the evolving role of artificial intelligence in predicting the severity of acute pancreatitis and supporting clinical decision-making, with a focus on surgical management. Recent advances show that data-driven models could improve early risk assessment compared to traditional methods. Although their potential clinical benefits are becoming increasingly clear, real-world implementation remains limited. Initial results are encouraging, but important questions regarding reliability, safety, and integration into clinical practice still need to be addressed.

Indexed as

acute pancreatitisartificial intelligencemachine learningpredictive modelsseverity predictionsurgery

Identifiers

PMID42122053
PMCPMC13163400

What OpenQuestion holds

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Registered trials

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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.