Letter to The Rise of Intelligent Plastic Surgery: A 10-Year Bibliometric Journey Through AI Applications, Challenges, and Transformative Potential.
TL;DR
Despite enthusiasm for workflow integration, many surgical AI models remain proof of concept without prospective, real-world evaluation, external validation, or post-deployment surveillance, raising concerns about generalizability and bias.
OpenAlex 토픽 ·
Artificial Intelligence in Healthcare and Education
Radiomics and Machine Learning in Medical Imaging
AI in cancer detection
【연구 목적】 인공지능(AI)을 활용한 성형외과 연구의 10년간의 서지학적 동향을 분석한 기존 논문에 대해, 단순한 인용 증가가 실제 임상적 영향력을 반영하는지 검증하고 AI 기술의 현실적 적용 가능성을 비판적으로 평가한다.
APA
Rong Zhang (2026). Letter to The Rise of Intelligent Plastic Surgery: A 10-Year Bibliometric Journey Through AI Applications, Challenges, and Transformative Potential.. Aesthetic plastic surgery, 50(6), 2346-2347. https://doi.org/10.1007/s00266-025-05261-5
MLA
Rong Zhang. "Letter to The Rise of Intelligent Plastic Surgery: A 10-Year Bibliometric Journey Through AI Applications, Challenges, and Transformative Potential.." Aesthetic plastic surgery, vol. 50, no. 6, 2026, pp. 2346-2347.
PMID
40965629
Abstract
This correspondence appraises "The Rise of Intelligent Plastic Surgery: A 10-Year Bibliometric Journey Through AI Applications, Challenges, and Transformative Potential." First, apparent "explosive" growth may reflect hype cycles, duplication, and editorial dynamics; impact should be triangulated with field-weighted citation impact, citation half-life, and distributions of study designs, including rates of external validation. Second, topic clusters derived from VOSviewer/CiteSpace risk conflating lexical proximity with practical centrality; calibration with expert Delphi panels and structured content analyses is needed to verify whether detected "hotspots"-shaped surgical decision making. Third, despite enthusiasm for workflow integration, many surgical AI models remain proof of concept without prospective, real-world evaluation, external validation, or post-deployment surveillance, raising concerns about generalizability and bias. Overall, the reviewed article offers a valuable quantitative foundation. LEVEL OF EVIDENCE IV: 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.
MeSH Terms
Humans; Surgery, Plastic; Bibliometrics; Artificial Intelligence; Plastic Surgery Procedures
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외부 PMID 2건 (DB 미수집)
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