Evidence map›Paper›PMID 40550899›Full record

ArticleAesthetic plastic surgery2025

Invited Response to "Artificial Intelligence in Rhinoplasty: Precision or Over-Reliance?"

Ariana Genovese, Srinivasagam Prabha, Antonio Jorge Forte

Abstract readLetter
PubMed Publisher
In one paragraph

Article in Aesthetic plastic surgery, 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

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

1 citing paper in PubMed.

  1. Review
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.

Ariana GenoveseDivision of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, USA.
Srinivasagam PrabhaDivision of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, USA.
Antonio Jorge ForteDivision of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, USA. ajvforte@yahoo.com.br.ORCID 0000-0003-2004-7538

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The integration of artificial intelligence (AI) into surgical practice demands critical reflection-not only on its capabilities, but also on its appropriate role. We appreciate De Bernardis et al. for raising essential concerns about the limits of AI in operative planning, particularly in contexts where human judgment, healing trajectories, and esthetic discernment defy algorithmic modeling. Rather than viewing these limitations as grounds for rejection, we argue that they clarify where AI truly belongs: not in replacing surgical expertise, but in reinforcing the systems that support it. When applied thoughtfully, AI can reduce cognitive burden, streamline repetitive tasks, and extend access to reliable, patient-centered information. Its value lies in augmentation, not replication. Yet this potential must be grounded in ethical design, where accountability, representational fairness, and data security are not add-ons, but foundational principles. The future of AI in rhinoplasty is not a binary of precision versus over-reliance. It is a challenge of integration: how to embed AI into the surgical ecosystem in ways that elevate care, protect patients, and preserve what is uniquely human in medicine.Level of Evidence V This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors   www.springer.com/00266 .

Indexed as

Artificial IntelligenceRhinoplastyHumansArtificial intelligenceClinical decision-makingEthics, medicalPlastic surgeryPostoperative careRhinoplasty

Identifiers

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.