Evidence map›Paper›PMID 40938407›Full record

ReviewChirurgie (Heidelberg, Germany)2025

[Multimodal data processing through AI: envisioning the operating room of the future].

Jennifer A Eckhoff, Dolores T Krauss, Stefanie Brunner, Christiane J Bruns, Hans F Fuchs

Abstract readEnglish AbstractReview
PubMed Publisher
In one paragraph

Review in Chirurgie (Heidelberg, Germany), 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

5 authors.

Jennifer A EckhoffKlinik für Allgemein‑, Viszeral‑, Thorax- und Transplantationschirurgie, Uniklinik Köln, Kerpener Straße 62, 50937, Köln, Deutschland. Jennifer.eckhoff@uk-koeln.de.
Dolores T KraussKlinik für Allgemein‑, Viszeral‑, Thorax- und Transplantationschirurgie, Uniklinik Köln, Kerpener Straße 62, 50937, Köln, Deutschland.
Stefanie BrunnerKlinik für Allgemein‑, Viszeral‑, Thorax- und Transplantationschirurgie, Uniklinik Köln, Kerpener Straße 62, 50937, Köln, Deutschland.
Christiane J BrunsKlinik für Allgemein‑, Viszeral‑, Thorax- und Transplantationschirurgie, Uniklinik Köln, Kerpener Straße 62, 50937, Köln, Deutschland.
Hans F FuchsKlinik für Allgemein‑, Viszeral‑, Thorax- und Transplantationschirurgie, Uniklinik Köln, Kerpener Straße 62, 50937, Köln, Deutschland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDespite intensive research the clinical implementation of artificial intelligence (AI) in surgery remains limited. In addition to infrastructural and regulatory barriers, this is due to the isolated processing of individual data sources (e.g. video), although the true potential of surgical AI lies in the integration of multimodal data.

objectiveWhat added value does AI-driven analysis of multimodal data offer in surgery, and how can it realistically be integrated into clinical practice?

methodThis review is based on first results on multimodal data acquisition and processing at University Hospital Cologne as well as a targeted literature search.

resultsThe integration of different data sources shows great potential; however, lack of infrastructure and regulation hinders the implementation. DISCUSSION: In addition to technological development, clear legal frameworks are required to enable the clinical integration of innovative AI systems.

Indexed as

Artificial IntelligenceOperating RoomsForecastingHumansArtificial intelligenceComputer-assisted surgeryMinimally invasive surgeryMultimodal imagingSurgical oncology

Identifiers

PMID40938407

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.