Evidence map›Paper›PMID 42138739›Full record

ReviewSurgical endoscopy2026

Incentivizing artificial intelligence in surgery.

Abdulrahman Alomar, Vikrom Dhar, Amin Madani, Simon Laplante

Abstract readReview
PubMed Publisher
In one paragraph

Review in Surgical endoscopy, 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

4 authors.

Abdulrahman AlomarDepartment of Surgery, Mayo Clinic, Rochester, MN, USA. alomar.abdulrahman@mayo.edu.ORCID http://orcid.org/0009-0000-2766-8383
Vikrom DharDepartment of Surgery, Northwell Health, Lenox Hill Hospital, New York, NY, USA.
Amin MadaniDepartment of Surgery, University of Toronto, Toronto, ON, Canada.
Simon LaplanteDepartment of Surgery, Mayo Clinic, Rochester, MN, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) is a rapidly growing technological advancement that can be used to transform surgical practice. Applications range from robotic assistance and computer vision, to perioperative guidance and administrative tasks. Despite the promising potential of AI in surgery, limitations to its adoption remains an issue.

objectiveThe aim of this article is to produce a framework for the incentivization of widespread adoption of artificial intelligence in surgery; taking into consideration the current landscape of AI in surgery, barriers to its adoption and challenges.

methodsA literature search was carried out of peer-reviewed articles, policy documents, and funding program reports that are relevant to the use of AI in healthcare.

conclusionStrategic financial and non-financial incentives frameworks, coupled with a continuous demonstration of clinical and economic value, are required for AI to transition from a promising innovation to a routine component of surgical practice.

Indexed as

Artificial IntelligenceHumansMotivationArtificial intelligenceDigital surgeryImplementation scienceIncentives frameworksSurgery

Identifiers

PMID42138739

What Socratic 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 Socratic graph.