Evidence map›Paper›PMID 40742577›Full record

ReviewJournal of robotic surgery2025

Artificial intelligence in hepatopancreatobiliary surgery for clinical outcome prediction: current perspective and future direction.

Javed Latif, Giuseppe Garcea, Ashley Dennison

Abstract readReview
PubMed Publisher
In one paragraph

Review in Journal of robotic surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Javed LatifDepartment of Hepatopancreatobiliary Surgery, Glenfield Hospital, University Hospitals of Leicester, Glenfield, Leicester, LE3 9QP, UK. javed.latif@nhs.net.ORCID http://orcid.org/0000-0002-8993-6547
Giuseppe GarceaDepartment of Hepatopancreatobiliary Surgery, Glenfield Hospital, University Hospitals of Leicester, Glenfield, Leicester, LE3 9QP, UK.
Ashley DennisonDepartment of Hepatopancreatobiliary Surgery, Glenfield Hospital, University Hospitals of Leicester, Glenfield, Leicester, LE3 9QP, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The expanding and evolving role of artificial intelligence (AI) in surgery has been enhanced by the adoption of robotic-assisted surgery (RAS), which provides a platform to facilitate the integration and utilisation of AI technologies. One area where AI is likely to be particularly valuable is outcome prediction using deep learning models (DLMs). This narrative review examines DLMs in hepatopancreatobiliary (HPB) surgery, highlighting their role in predicting postoperative complications and surgical complexity with an increased level of accuracy compared with traditional methods. In addition to reviewing existing literature, this article offers a forward-looking perspective on emerging innovations such as real-time intraoperative guidance, federated learning for global collaboration, and the development of explainable AI frameworks. By addressing challenges related to data quality, model generalisability, and ethical implementation, AI has the potential to transform HPB surgery and deliver more personalised, precise, and equitable care.

Indexed as

Artificial IntelligenceBiliary Tract Surgical ProceduresDigestive System Surgical ProceduresRobotic Surgical ProceduresDeep LearningHumansPostoperative ComplicationsTreatment OutcomeArtificial intelligenceDeep learning models: clinical outcomeHepatopancreatobiliary surgeryPrediction models

Identifiers

PMID40742577

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.