Evidence map›Paper›PMID 36479133›Full record

ArticlePlastic and reconstructive surgery. Global open2022

A Systematic Review of Artificial Intelligence Applications in Plastic Surgery: Looking to the Future.

Daisy L Spoer, Julianne M Kiene, Paige K Dekker, Samuel S Huffman, Kevin G Kim, Andrew I Abadeer, Kenneth L Fan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Plastic and reconstructive surgery. Global open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
–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

21 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  11. AI-Based Facial Analysis vs Self-Report: A Pilot Study Using Insights From Pre-Procedural Psychological Tests.Medical science monitor : international medical journal of experimental and clinical research · 2025
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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.

Daisy L SpoerGeorgetown University School of Medicine, Washington, D.C.
Julianne M KieneGeorgetown University School of Medicine, Washington, D.C.
Paige K DekkerDivision of Plastic Surgery, University of Southern California School of Medicine, Los Angeles, Ca.
Samuel S HuffmanGeorgetown University School of Medicine, Washington, D.C.
Kevin G KimDepartment of Plastic Surgery, New York University Langone Health, New York, N.Y.
Andrew I AbadeerDepartment of Plastic and Reconstructive Surgery, MedStar Georgetown University Hospital, Washington, D.C.
Kenneth L FanDepartment of Plastic and Reconstructive Surgery, MedStar Georgetown University Hospital, Washington, D.C.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is presently employed in several medical specialties, particularly those that rely on large quantities of standardized data. The integration of AI in surgical subspecialties is under preclinical investigation but is yet to be widely implemented. Plastic surgeons collect standardized data in various settings and could benefit from AI. This systematic review investigates the current clinical applications of AI in plastic and reconstructive surgery. Methods: A comprehensive literature search of the Medline, EMBASE, Cochrane, and PubMed databases was conducted for AI studies with multiple search terms. Articles that progressed beyond the title and abstract screening were then subcategorized based on the plastic surgery subspecialty and AI application. Results: The systematic search yielded a total of 1820 articles. Forty-four studies met inclusion criteria warranting further analysis. Subcategorization of articles by plastic surgery subspecialties revealed that most studies fell into aesthetic and breast surgery (27%), craniofacial surgery (23%), or microsurgery (14%). Analysis of the research study phase of included articles indicated that the current research is primarily in phase 0 (discovery and invention; 43.2%), phase 1 (technical performance and safety; 27.3%), or phase 2 (efficacy, quality improvement, and algorithm performance in a medical setting; 27.3%). Only one study demonstrated translation to clinical practice. Conclusions: The potential of AI to optimize clinical efficiency is being investigated in every subfield of plastic surgery, but much of the research to date remains in the preclinical status. Future implementation of AI into everyday clinical practice will require collaborative efforts.

Identifiers

PMID36479133
PMCPMC9722565

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Registered trials

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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.