Evidence map›Paper›PMID 42592424›Full record

ArticlePlastic and reconstructive surgery. Global open2026

Plastic Surgery Search Engine Optimization in the Artificial Intelligence Era.

Ravi Dhawan, Albert Losken, Orr Shauly

Abstract read
In one paragraph

Article in Plastic and reconstructive surgery. Global open, 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

3 authors.

Ravi DhawanFrom the Division of Plastic Surgery, Emory University School of Medicine, Atlanta, GA.
Albert LoskenFrom the Division of Plastic Surgery, Emory University School of Medicine, Atlanta, GA.
Orr ShaulyFrom the Division of Plastic Surgery, Emory University School of Medicine, Atlanta, GA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Marketing in aesthetic plastic surgery has shifted from traditional print and television toward digital-first strategies, with social media and search engine optimization (SEO) now shaping how patients engage with practices. More than 90% of cosmetic patients seek information through web searches and social platforms before a consultation. Artificial intelligence (AI) is accelerating this transformation by automating keyword research, trend forecasting, and content generation, making advanced SEO strategies more accessible to individual surgeons and small practices. AI-enhanced SEO tools enable efficient targeting of high-intent patients, improve patient literacy through optimized content, and allow surgeons to maintain visibility in a competitive digital marketplace. Automation of content creation, however, raises concerns regarding misinformation, unrealistic expectations, and ethical breaches in advertising. International audits of cosmetic marketing already reveal frequent omissions of risk information and the reinforcement of narrow beauty ideals, pitfalls that AI may inadvertently amplify. Only 2 of the 8 platforms reviewed are marketed with explicit Health Insurance Portability and Accountability Act (HIPAA)-aligned infrastructure. Surgeons must remain actively involved in reviewing and guiding AI-generated materials to ensure accuracy, transparency, and alignment with professional standards. Integrating AI-driven SEO within an ethical framework that prioritizes informed consent, balanced representation, and patient autonomy represents an opportunity for the specialty to lead in setting responsible standards for digital patient communication.

Identifiers

PMID42592424
PMCPMC13466220

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.