Evidence mapPaperPMID 42540561Full record

ReviewAesthetic surgery journal. Open forum2026

Artificial Intelligence in Plastic Surgery: A Bibliometric and Visual Analysis of the 100 Most-Cited English-Language Publications.

Jonathan Mokhtar, Meera Hallak, Michel Gabriel Cazenave, Lucas Kreutz-Rodrigues, Krishna S Vyas, Curtis L Cetrulo, Alexandre G Lellouch

Abstract readReview
In one paragraph

Review in Aesthetic surgery journal. Open forum, 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

7 authors.

Meera Hallak
Michel Gabriel Cazenave
Krishna S Vyas
Curtis L Cetrulo

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is fundamentally transforming the landscape of plastic surgery, yet the structural architecture of its most influential scholarship has not been systematically characterized. This study presents a bibliometric and visual analysis of the 100 most-cited English-language AI publications in plastic surgery. Scopus was searched from database inception through January 15, 2026. Eligible articles underwent dual independent screening in Covidence, and the 100 most-cited publications were analyzed using R (v4.4.1) and VOSviewer (v1.6.18) for citation metrics, authorship networks, geographic and institutional contributions, journal distribution, and thematic categorization. Of the 3827 retrieved records, 357 met the full inclusion criteria, and the top 100 were identified. These articles collectively received 2701 citations (average: 27.01 ± 22.19), with a marked post-2022 publication surge accounting for 79% of the studies, with 2024 contributing to 34% of that share. Patient education and large language model-based consultation constituted the dominant thematic cluster (35%), followed by ethical and governance considerations (17%) and outcomes prediction and risk modeling (13%). Aesthetic surgery represented the most prolific specialty (34%), with craniofacial and breast reconstruction each contributing to 21%. Aesthetic Plastic Surgery (

Identifiers

PMID42540561
PMCPMC13426317

What Socratic 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 Socratic graph.