Evidence map›Paper›PMID 40285450›Full record

ReviewPhysiological reports2025

Artificial intelligence in colorectal surgery multidisciplinary team approach-From innovation to application.

Ali Murtada, Fatima Kayali, Matti Jubouri, Samuel N S Ghattas, Samuel S S Rezk, Feroze Ahmed Mir, Ian Williams, Mohamad Bashir, Damian M Bailey

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Physiological reports, 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

9 authors.

Ali MurtadaDepartment of General Surgery, Ysbyty Glan Clwyd, Rhyl, UK.
Fatima KayaliUniversity Hospitals Sussex, Worthing, West Sussex, UK.
Matti JubouriHull York Medical School, University of York, York, UK.ORCID 0000-0001-6725-6164
Samuel N S GhattasDepartment of General Surgery, Ysbyty Glan Clwyd, Rhyl, UK.
Samuel S S RezkDepartment of General Surgery, Ysbyty Glan Clwyd, Rhyl, UK.
Feroze Ahmed MirDepartment of General Surgery, Ysbyty Glan Clwyd, Rhyl, UK.
Ian WilliamsDepartment of Vascular Surgery, University Hospital of Wales, Cardiff, UK.
Mohamad BashirNeurovascular Research Laboratory, Faculty of Life Sciences and Education, University of South Wales, Pontypridd, UK.
Damian M BaileyNeurovascular Research Laboratory, Faculty of Life Sciences and Education, University of South Wales, Pontypridd, UK.ORCID 0000-0003-0498-7095

Funding

Royal Society Wolfson Research Fellowship WM170007
6 · The paper itself

Abstract

Artificial intelligence (AI) has played a novel role in aiding healthcare system functions and enhancing the patient experience. Multidisciplinary teams (MDT) have become an integral part of disease and management planning, especially with the rising number of our aging population and the paucity of sufficient resources. The incorporation of MDTs facilitates a holistic approach to patient care, encompassing the physical, psychological, and social needs of patients and their families. Particularly with the growing number of colorectal cancer diagnoses, notably among the younger populations, the utilization of AI in the colorectal MDT holds great potential value. The ability to enhance the quality of these interdisciplinary discussions will likely reflect on improving holistic patient-centered care and reducing the numbers of late or misdiagnosis. In addition, the incorporation of AI into these meetings will aid in reducing the workload on healthcare professionals and reduce the financial burden on pressurized healthcare systems. This narrative review article explores the role of AI in the colorectal surgery MDT, its drawbacks, and its merits.

Indexed as

Artificial IntelligenceColorectal NeoplasmsColorectal SurgeryPatient Care TeamHumansartificial intelligenceclinical decision‐makingcolorectal Cancercolorectal surgerymulti‐disciplinary team

Identifiers

PMID40285450
PMCPMC12032448

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.