Evidence map›Paper›PMID 38977450›Full record

ArticleAesthetic plastic surgery2025

Response to "Letter to the Editor-Exploring the Unknown: Evaluating ChatGPT's Performance in Uncovering Novel Aspects of Plastic Surgery and Identifying Areas for Future Innovation".

Ishith Seth, Bryan Lim, Warren M Rozen

Abstract readLetterComment
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. 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

5 · Who and what money

Authors and funding

3 authors.

Ishith SethDepartment of Plastic Surgery, Peninsula Health, Melbourne, VIC, 3199, Australia.
Bryan LimDepartment of Plastic Surgery, Peninsula Health, Melbourne, VIC, 3199, Australia. lim.bryan58@gmail.com.ORCID 0009-0007-9647-5180
Warren M RozenDepartment of Plastic Surgery, Peninsula Health, Melbourne, VIC, 3199, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We appreciate Dr. Qi and Dr. Niu for their insightful comments on our study, "Exploring the Unknown: Evaluating ChatGPT's Performance in Uncovering Novel Aspects of Plastic Surgery and Identifying Areas for Future Innovation." Their observations underscore significant considerations in the application of artificial intelligence (AI) in plastic surgery. We agree with their concern about potential biases in ChatGPT's responses. The AI's frequent attribution of the title "parent of plastic surgery" to Sir Harold Delf Gillies, despite gender-neutral terminology, highlights underlying biases from training data. These biases often reflect historical texts and contemporary writings. Addressing them requires refining training datasets for balanced representation and developing algorithms that adjust dynamically to diverse inputs. The authors also question the criteria ChatGPT uses to identify key contributions to plastic surgery. The AI's focus on microsurgery, minimally invasive techniques, and tissue engineering, while significant, may prioritize keyword prevalence over a holistic evaluation. Enhancing ChatGPT's capabilities through targeted training and input from subject matter experts could improve the AI's ability to generate more balanced outputs. The identified bias favoring reconstructive over cosmetic procedures is another critical point. While reconstructive advancements are transformative, cosmetic surgery also has significant innovations. Ensuring ChatGPT presents a balanced view of both reconstructive and cosmetic advancements is essential. This can be achieved by diversifying training data and calibrating the AI to give equitable weight to different subspecialties within plastic surgery. AI models like ChatGPT are proficient in processing and generating information but lack the human elements of creativity, intuition, and emotional depth critical for groundbreaking innovations. AI should complement, not replace, the expert judgment and innovative thinking of skilled plastic surgeons. Ensuring the accuracy of AI-generated responses is crucial. Clinicians must verify AIgenerated information against established medical literature and clinical guidelines to maintain accuracy in medical practice. Continuous feedback and improvement mechanisms are vital to enhance AI's clinical utility. The improvement of AI in plastic surgery will be driven by active involvement from surgeons, providing comprehensive and balanced data for training to ensure AI systems evolve to support and enhance clinical practice effectively.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 IntelligencePlastic Surgery ProceduresSurgery, PlasticGenerative Artificial IntelligenceHumans

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.