Evidence map›Paper›PMID 40807006›Full record

ReviewJournal of clinical medicine2025

Technological Advances in Pre-Operative Planning.

Mikolaj R Kowal, Mohammed Ibrahim, André L Mihaljević, Philipp Kron, Peter Lodge

Abstract readReview
In one paragraph

Review in Journal of clinical medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
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.

Mikolaj R KowalLeeds Institute of Medical Research, University of Leeds, Leeds LS9 7TF, UK.ORCID 0000-0001-5628-4880
Mohammed IbrahimUniversity Clinic for General, Visceral and Transplantation Surgery, University Hospital Tübingen, 72076 Tübingen, Germany.ORCID 0009-0007-3466-6790
André L MihaljevićUniversity Clinic for General, Visceral and Transplantation Surgery, University Hospital Tübingen, 72076 Tübingen, Germany.
Philipp KronUniversity Clinic for General, Visceral and Transplantation Surgery, University Hospital Tübingen, 72076 Tübingen, Germany.ORCID 0000-0002-6074-7072
Peter LodgeLeeds Institute of Medical Research, University of Leeds, Leeds LS9 7TF, UK.

Funding

European Union 101137233
6 · The paper itself

Abstract

Surgery remains a healthcare intervention with significant risks for patients. Novel technologies can now enhance the peri-operative workflow, with artificial intelligence (AI) and extended reality (XR) to assist with pre-operative planning. This review focuses on innovation in AI, XR and imaging for hepato-biliary surgery planning. The clinical challenges in hepato-biliary surgery arise from heterogeneity of clinical presentations, the need for multiple imaging modalities and highly variable local anatomy. AI-based models have been developed for risk prediction and multi-disciplinary tumor (MDT) board meetings. The future could involve an on-demand and highly accurate AI-powered decision tool for hepato-biliary surgery, assisting the surgeon to make the most informed decision on the treatment plan, conferring the best possible outcome for individual patients. Advances in AI can also be used to automate image interpretation and 3D modelling, enabling fast and accurate 3D reconstructions of patient anatomy. Surgical navigation systems utilizing XR are already in development, showing an early signal towards improved patient outcomes when used for hepato-biliary surgery. Live visualization of hepato-biliary anatomy in the operating theatre is likely to improve operative safety and performance. The technological advances in AI and XR provide new applications in pre-operative planning with potential for patient benefit. Their use in surgical simulation could accelerate learning curves for surgeons in training. Future research must focus on standardization of AI and XR study reporting, robust databases that are ethically and data protection-compliant, and development of inter-disciplinary tools for various healthcare applications and systems.

Indexed as

3D imagingAR in surgeryartificial intelligence in surgeryextended realitiesrobotic surgerysurgical navigationsurgical training

Identifiers

PMID40807006
PMCPMC12347794

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.