Transforming endoscopic retrograde cholangiopancreatography: the role of artificial intelligence in pre-operative planning, intraoperative navigation, and post-operative risk prediction.

Yaoqi Wang, Ya Peng, Zheng Wang, Peng Liu, Zhiyuan Chen

Abstract readReview

In one paragraph

Review in Frontiers in medicine, 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

5 authors.

Yaoqi WangDepartment of Gastroenterology, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, Hunan, China.

Ya PengDepartment of Gastroenterology, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, Hunan, China.

Zheng WangSchool of Computer Science, Hunan First Normal University, Changsha, China.

Peng LiuDepartment of Gastroenterology, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, Hunan, China.

Zhiyuan ChenDepartment of Gastroenterology, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, Hunan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Endoscopic Retrograde Cholangiopancreatography (ERCP) is a technically demanding procedure for pancreaticobiliary diseases. Artificial intelligence (AI) is transforming ERCP by improving diagnostic accuracy, procedural safety, and postoperative risk prediction. This review synthesizes AI applications across the entire workflow: preoperative planning using multimodal risk models, intraoperative navigation via real-time anatomical localization and augmented reality, and postoperative complication prediction with dynamic algorithms. AI also optimizes anesthesia and ambulatory surgery workflows. Challenges remain in data standardization, legal accountability, and clinical integration. We advocate for multicenter collaboration, ethical oversight, and comprehensive patient management systems to establish AI-assisted ERCP as a new standard for personalized interventional care.

Indexed as

artificial intelligenceendoscopic retrograde cholangiopancreatographyintraoperative navigationpostoperative risk predictionpreoperative planning

Identifiers

PMID42558445

DOI10.3389/fmed.2026.1879402

PMCPMC13437472

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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.