Evidence map›Paper›PMID 40672785›Full record

ArticlePlastic and reconstructive surgery. Global open2025

Artificial Intelligence-assisted Risk Stratification in Aesthetic Surgery: A Prospective Observational Study.

Williams E Bukret

Abstract read
In one paragraph

Article in Plastic and reconstructive surgery. Global open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

1 author.

Williams E BukretFrom Private Practice, Bukret Plastic Surgery, Buenos Aires, Argentina.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Enhancing patient safety and minimizing complications are critical objectives in aesthetic surgery. In 2021, the author developed and validated an artificial intelligence (AI)-assisted risk stratification model to predict complications and support clinical decision-making. This study aimed to evaluate the clinical impact of surgical risk stratification on patient outcomes. Methods: A prospective observational study was conducted from January 2021 to May 2024 to assess 3347 patients, using an AI model. The patients were stratified into high-, moderate-, and low-risk groups and received tailored recommendations. A total of 74 patients proceeded with surgery, and their outcomes were analyzed. Statistical analyses included logistic regression and correlation tests ( Results: Of the 3347 patients assessed, 18.55% were high-risk, 30.56% were moderate-risk, and 50.88% were low-risk patients. Among the 74 patients who underwent surgery, 7 (9.46%) developed 11 complications, with the high-risk group showing a relative risk of 6.73. Logistic regression confirmed that age and Caprini score were independent risk factors, whereas body mass index and smoking showed no statistical association with complications, likely because of effective preoperative risk mitigation, including weight optimization and smoking cessation protocols enforced by the AI model. Conclusions: This study demonstrated that AI-assisted risk stratification effectively identifies risk factors in aesthetic surgery, enabling personalized preoperative recommendations to mitigate complications. AI can enhance patient safety and surgical outcomes by enabling systematic risk stratification. Integrating AI into surgical planning optimizes patient selection and supports its implementation in clinical decision-making.

Identifiers

PMID40672785
PMCPMC12266908

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LicenceCC BY-NC-ND
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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.