Evidence map›Paper›PMID 37770637›Full record

ArticleAesthetic plastic surgery2024

Exploring the Potential of ChatGPT-4 in Responding to Common Questions About Abdominoplasty: An AI-Based Case Study of a Plastic Surgery Consultation.

Wenbo Li, Junjiang Chen, Fengmin Chen, Jiaqing Liang, Hongyu Yu

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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 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

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

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Wenbo LiDepartment of Nursing, Jinzhou Medical University, Jinzhou, 121001, China.
Junjiang ChenDepartment of Burn Plastic and Medical Aesthetic Surgery, The First Affiliated Hospital, Jinzhou Medical University, Jinzhou, China.
Fengmin ChenDepartment of Colorectal Surgery, The First Affiliated Hospital, Jinzhou Medical University, Jinzhou, China.
Jiaqing LiangDepartment of Nursing, Jinzhou Medical University, Jinzhou, 121001, China.
Hongyu YuDepartment of Nursing, Jinzhou Medical University, Jinzhou, 121001, China. 15840040053@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith the increasing integration of artificial intelligence (AI) in health care, AI chatbots like ChatGPT-4 are being used to deliver health information.

objectivesThis study aimed to assess the capability of ChatGPT-4 in answering common questions related to abdominoplasty, evaluating its potential as an adjunctive tool in patient education and preoperative consultation.

methodsA variety of common questions about abdominoplasty were submitted to ChatGPT-4. These questions were sourced from a question list provided by the American Society of Plastic Surgery to ensure their relevance and comprehensiveness. An experienced plastic surgeon meticulously evaluated the responses generated by ChatGPT-4 in terms of informational depth, response articulation, and competency to determine the proficiency of the AI in providing patient-centered information.

resultsThe study showed that ChatGPT-4 can give clear answers, making it useful for answering common queries. However, it struggled with personalized advice and sometimes provided incorrect or outdated references. Overall, ChatGPT-4 can effectively share abdominoplasty information, which may help patients better understand the procedure. Despite these positive findings, the AI needs more refinement, especially in providing personalized and accurate information, to fully meet patient education needs in plastic surgery.

conclusionsAlthough ChatGPT-4 shows promise as a resource for patient education, continuous improvements and rigorous checks are essential for its beneficial integration into healthcare settings. The study emphasizes the need for further research, particularly focused on improving the personalization and accuracy of AI responses. 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

AbdominoplastyArtificial IntelligenceReferral and ConsultationFemaleHumansPatient Education as TopicSurgery, PlasticSurveys and QuestionnairesAbdominoplastyArtificial intelligenceChatbotChatGPTChatGPT-4

Identifiers

PMID37770637

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.