Evidence map›Paper›PMID 38589561›Full record

ArticleAesthetic plastic surgery2024

Blepharoptosis Consultation with Artificial Intelligence: Aesthetic Surgery Advice and Counseling from Chat Generative Pre-Trained Transformer (ChatGPT).

Makoto Shiraishi, Koji Tanigawa, Yoko Tomioka, Ami Miyakuni, Yuta Moriwaki, Rui Yang, Jun Oba, Mutsumi Okazaki

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Article in Aesthetic plastic surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

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

  1. Pooled it
  2. Article
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  4. Comparative analysis of artificial intelligence platforms in generating Post-Operative instructions for endoscopic transnasal skull base surgery.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026
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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

8 authors.

Makoto Shiraishi *Department of Plastic and Reconstructive Surgery, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan. shiraishi-kyf@umin.ac.jp.ORCID 0000-0002-3734-2085
Koji Tanigawa *Department of Plastic and Reconstructive Surgery, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
Yoko TomiokaDepartment of Plastic and Reconstructive Surgery, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
Ami MiyakuniDepartment of Plastic and Reconstructive Surgery, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
Yuta MoriwakiDepartment of Plastic and Reconstructive Surgery, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
Rui YangDepartment of Plastic and Reconstructive Surgery, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
Jun ObaDepartment of Plastic and Reconstructive Surgery, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
Mutsumi OkazakiDepartment of Plastic and Reconstructive Surgery, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChat generative pre-trained transformer (ChatGPT) is a publicly available extensive artificial intelligence (AI) language model that leverages deep learning to generate text that mimics human conversations. In this study, the performance of ChatGPT was assessed by offering insightful and precise answers to a series of fictional questions and emulating a preliminary consultation on blepharoplasty.

methodsChatGPT was posed with questions derived from a blepharoplasty checklist provided by the American Society of Plastic Surgeons. Board-certified plastic surgeons and non-medical staff members evaluated the responses for accuracy, informativeness, and accessibility.

resultsNine questions were used in this study. Regarding informativeness, the average score given by board-certified plastic surgeons was significantly lower than that given by non-medical staff members (2.89 ±  0.72 vs 4.41 ± 0.71; p = 0.042). No statistically significant differences were observed in accuracy (p = 0.56) or accessibility (p = 0.11).

conclusionsOur results emphasize the effectiveness of ChatGPT in simulating doctor-patient conversations during blepharoplasty. Non-medical individuals found its responses more informative compared with the surgeons. Although limited in terms of specialized guidance, ChatGPT offers foundational surgical information. Further exploration is warranted to elucidate the broader role of AI in esthetic surgical consultations. LEVEL OF EVIDENCE V: Observational study under respected authorities. 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 IntelligenceBlepharoplastyBlepharoptosisAdultCounselingFemaleHumansMalePhysician-Patient RelationsReferral and ConsultationSurgery, PlasticArtificial intelligenceBlepharoplastyBlepharoptosisChatGPTOpen AI

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.