Preoperative Patient Guidance and Education in Aesthetic Breast Plastic Surgery: A Novel Proposed Application of Artificial Intelligence Large Language Models.
Jad Abi-Rafeh, Brian Bassiri-Tehrani, Roy Kazan, Heather Furnas, Dennis Hammond, William P Adams, Foad Nahai
{"title":"Preoperative Patient Guidance and Education in Aesthetic Breast Plastic Surgery: A Novel Proposed Application of Artificial Intelligence Large Language Models.","authors":"Jad Abi-Rafeh, Brian Bassiri-Tehrani, Roy Kazan, Heather Furnas, Dennis Hammond, William P Adams, Foad Nahai","doi":"10.1093/asjof/ojae062","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>At a time when Internet and social media use is omnipresent among patients in their self-directed research about their medical or surgical needs, artificial intelligence (AI) large language models (LLMs) are on track to represent hallmark resources in this context.</p><p><strong>Objectives: </strong>The authors aim to explore and assess the performance of a novel AI LLM in answering questions posed by simulated patients interested in aesthetic breast plastic surgery procedures.</p><p><strong>Methods: </strong>A publicly available AI LLM was queried using simulated interactions from the perspective of patients interested in breast augmentation, mastopexy, and breast reduction. Questions posed were standardized and categorized under aesthetic needs inquiries and awareness of appropriate procedures; patient candidacy and indications; procedure safety and risks; procedure information, steps, and techniques; patient assessment; preparation for surgery; postprocedure instructions and recovery; and procedure cost and surgeon recommendations. Using standardized Likert scales ranging from 1 to 10, 4 expert breast plastic surgeons evaluated responses provided by AI. A postparticipation survey assessed expert evaluators' experience with LLM technology, perceived utility, and limitations.</p><p><strong>Results: </strong>The overall performance across all question categories, assessment criteria, and procedures examined was 7.3/10 ± 0.5. Overall accuracy of information shared was scored at 7.1/10 ± 0.5; comprehensiveness at 7.0/10 ± 0.6; objectivity at 7.5/10 ± 0.4; safety at 7.5/10 ± 0.4; communication clarity at 7.3/10 ± 0.2; and acknowledgment of limitations at 7.7/10 ± 0.2. With regards to performance on procedures examined, the model's overall score was 7.0/10 ± 0.8 for breast augmentation; 7.6/10 ± 0.5 for mastopexy; and 7.4/10 ± 0.5 for breast reduction. The score on breast implant-specific knowledge was 6.7/10 ± 0.6.</p><p><strong>Conclusions: </strong>Albeit not without limitations, AI LLMs represent promising resources for patient guidance and patient education. The technology's machine learning capabilities may explain its improved performance efficiency.</p><p><strong>Level of evidence 4: </strong></p>","PeriodicalId":72118,"journal":{"name":"Aesthetic surgery journal. Open forum","volume":"6 ","pages":"ojae062"},"PeriodicalIF":0.0000,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11385898/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Aesthetic surgery journal. Open forum","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/asjof/ojae062","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Background: At a time when Internet and social media use is omnipresent among patients in their self-directed research about their medical or surgical needs, artificial intelligence (AI) large language models (LLMs) are on track to represent hallmark resources in this context.
Objectives: The authors aim to explore and assess the performance of a novel AI LLM in answering questions posed by simulated patients interested in aesthetic breast plastic surgery procedures.
Methods: A publicly available AI LLM was queried using simulated interactions from the perspective of patients interested in breast augmentation, mastopexy, and breast reduction. Questions posed were standardized and categorized under aesthetic needs inquiries and awareness of appropriate procedures; patient candidacy and indications; procedure safety and risks; procedure information, steps, and techniques; patient assessment; preparation for surgery; postprocedure instructions and recovery; and procedure cost and surgeon recommendations. Using standardized Likert scales ranging from 1 to 10, 4 expert breast plastic surgeons evaluated responses provided by AI. A postparticipation survey assessed expert evaluators' experience with LLM technology, perceived utility, and limitations.
Results: The overall performance across all question categories, assessment criteria, and procedures examined was 7.3/10 ± 0.5. Overall accuracy of information shared was scored at 7.1/10 ± 0.5; comprehensiveness at 7.0/10 ± 0.6; objectivity at 7.5/10 ± 0.4; safety at 7.5/10 ± 0.4; communication clarity at 7.3/10 ± 0.2; and acknowledgment of limitations at 7.7/10 ± 0.2. With regards to performance on procedures examined, the model's overall score was 7.0/10 ± 0.8 for breast augmentation; 7.6/10 ± 0.5 for mastopexy; and 7.4/10 ± 0.5 for breast reduction. The score on breast implant-specific knowledge was 6.7/10 ± 0.6.
Conclusions: Albeit not without limitations, AI LLMs represent promising resources for patient guidance and patient education. The technology's machine learning capabilities may explain its improved performance efficiency.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
