Evidence map›Paper›PMID 42162215›Full record

ArticleNPJ digital medicine2026

Optimizing AI implementation for surgery: recommendations for infrastructure and deployment in the operating room.

Ariana Walji, Caterina Masino, Stephanie Williams, Spencer Gable-Cook, Jimmy Qiu, Pouria Mashouri, Divya Kewalramani, Mayur Narayan, Tyler J Loftus, Wagner H Souza and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 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

11 authors.

Ariana WaljiSurgical Artificial Intelligence Research Academy, University Health Network, Toronto, ON, Canada.ORCID http://orcid.org/0009-0009-8519-4628
Caterina MasinoSurgical Artificial Intelligence Research Academy, University Health Network, Toronto, ON, Canada.ORCID http://orcid.org/0009-0001-7256-1750
Stephanie WilliamsTechna, University Health Network, Toronto, ON, Canada.ORCID http://orcid.org/0000-0003-2799-7624
Spencer Gable-CookTechna, University Health Network, Toronto, ON, Canada.ORCID http://orcid.org/0009-0004-2562-9320
Jimmy QiuTechna, University Health Network, Toronto, ON, Canada.ORCID http://orcid.org/0000-0002-3686-3081
Pouria MashouriDATA Team, University Health Network, Toronto, ON, Canada.ORCID http://orcid.org/0000-0003-4332-530X
Divya KewalramaniDivision of Acute Care Surgery, Department of Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA.ORCID http://orcid.org/0009-0004-9125-0909
Mayur NarayanDivision of Acute Care Surgery, Department of Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA.ORCID http://orcid.org/0000-0002-7232-7446
Tyler J LoftusDepartment of Surgery, University of Florida, Gainesville, FL, USA.ORCID http://orcid.org/0000-0001-5354-443X
Wagner H SouzaSurgical Artificial Intelligence Research Academy, University Health Network, Toronto, ON, Canada.ORCID http://orcid.org/0000-0003-3982-5475
Amin MadaniSurgical Artificial Intelligence Research Academy, University Health Network, Toronto, ON, Canada. amin.madani@uhn.ca.ORCID http://orcid.org/0000-0003-0901-9851

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent years have seen a surge in the development of Artificial Intelligence (AI) technologies to enhance intraoperative decision-making and improve patient safety. Yet, real-world evidence on their implementation remains limited. This study evaluated the impact of AI deployment in the operating room across key implementation domains-usability, cost, and carbon footprint-using cloud and edge-based infrastructures. Twenty-three end-users (surgeons, residents, fellows, operating room nurses) from a multi-site teaching hospital in Canada participated in system usability testing, assessed with validated scales and open-ended feedback. Cost and carbon footprint were assessed based on operational metrics. Results showed greater deployment task completion with Cloud but increased physical strain, carbon emissions, and operational costs compared to Edge after 396 h of use. Despite similar usability ratings, participants suggested improvements to the Cloud setup and both systems' interface. Findings provide evidence-based recommendations toward the implementation of sustainable, user-centered AI systems in surgical practice.

Identifiers

PMID42162215
PMCPMC13462103

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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.