Evidence map›Paper›PMID 42547631›Full record

ArticleSurgical endoscopy2026

The digital scalpel: navigating the future of surgical innovation.

Derek T O'Keeffe

Abstract read
In one paragraph

Article 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

1 author.

Derek T O'KeeffeUniversity of Galway, Galway, Ireland. Derek.t.okeeffe@universityofgalway.ie.ORCID http://orcid.org/0000-0001-8501-2382

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence is entering surgical practice not as an extension of the surgeon's hand, eyes, or dexterity, but of surgical judgment itself. This creates a growing informational asymmetry between well-resourced and under-resourced operative environments, with implications for training, competence, accountability, and equity.

methodsThis commentary, derived from the Karl Storz Lecture delivered at the SAGES 2026 Annual Meeting, proposes the Digital Scalpel: a five-layer conceptual architecture (data infrastructure, connected systems, computational vision, cognitive augmentation, and human judgment) for understanding AI-enabled surgical practice. The current evidence base for each layer is appraised, with particular reference to AI assessment of the Critical View of Safety in laparoscopic cholecystectomy.

resultsComputational vision is the most mature layer, with deep-learning systems demonstrating high concordance with expert assessment of the Critical View of Safety, though validation remains largely confined to elective, uninflamed, single-centre cases. Three risks are identified: automation complacency, an unresolved accountability gap, and a digital inverse care law under which AI-augmented capability tracks existing resource inequities. The most evidence-supported entry point for most institutions is routine operative video capture and structured review, not real-time guidance.

conclusionsThe surgeon of 2035 will require four attributes: preserved technical excellence, data literacy as a core competency, ethical fluency, and institutionalised clinician-engineer collaboration. The Digital Scalpel demands active, critical, and equitable engagement rather than passive technological adoption.

Indexed as

Artificial IntelligenceCholecystectomy, LaparoscopicSurgical InstrumentsHumansArtificial intelligenceDigital health equityDigital surgeryIntraoperative guidanceMachine learningSurgical innovationSurgical training

Identifiers

PMID42547631
PMCPMC13582378

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.