From Bench to Bedside: The Path Toward Real-World Translation for Artificial Intelligence in Pancreatic Cancer Detection.

Emir A Syailendra, Hajra Arshad, Felipe Lopez-Ramirez, Florent Tixier, Satomi Kawamoto, Elliot K Fishman, Linda C Chu

Abstract readReview

In one paragraph

Review in Korean journal of radiology, 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

7 authors.

Emir A Syailendra *Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.ORCID https://orcid.org/0000-0001-8608-533X

Hajra Arshad *Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.ORCID https://orcid.org/0000-0001-9206-739X

Felipe Lopez-RamirezRussell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.ORCID https://orcid.org/0000-0002-1560-9172

Florent TixierRussell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.ORCID https://orcid.org/0000-0002-9668-6574

Satomi KawamotoRussell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.ORCID https://orcid.org/0000-0002-3577-1388

Elliot K FishmanRussell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.ORCID https://orcid.org/0000-0002-2567-1658

Linda C ChuRussell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD, USA. lchu1@jhmi.edu.ORCID https://orcid.org/0000-0001-9729-2756

Funding

IPMN Global FoundationLustgarten Foundation

6 · The paper itself

Abstract

Patients with pancreatic cancer have low survival rates, largely because patients are diagnosed at an advanced stage. Current strategies for early detection, including imaging, blood tests, and genetic sequencing, have limited performance. Recent advances in artificial intelligence (AI) have shown that AI models can identify subtle pre-diagnostic imaging changes that may not be visible to radiologists, raising the possibility of earlier and more consistent pancreatic cancer detection. Despite this progress, real-world implementation of AI for pancreatic cancer detection remains limited. Most models struggle with reproducibility and generalizability across different institutions. Few have undergone prospective validation, and practical issues such as workflow integration, financial constraints, and continuous model monitoring remain unresolved. This article reviews the current state of AI for pancreatic cancer detection and outlines barriers beyond model specifics that must be addressed to enable clinical translation.

Indexed as

Artificial IntelligencePancreatic NeoplasmsDeep LearningEarly Detection of CancerHumansReproducibility of ResultsAdenocarcinomaArtificial intelligenceClinical implementationDeep learningDiagnosisEarly detectionNeoplasmPancreasPancreatic cancer

Identifiers

PMID42225574

DOI10.3348/kjr.2026.0003

PMCPMC13236442

What Socratic holds

Textmetadata

LicenceCC BY-NC

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 Socratic graph.