Aesthetic Plastic Surgery最新文献

This letter responds to the important work of Bastaninejad et al., highlighting how advanced large language models (LLMs) such as ChatGPT and Gemini perform in the emotionally and technically demanding landscape of revision rhinoplasty consultations. While the study demonstrates the remarkable communicative, empathetic, and informational strengths of general-purpose LLMs, it also exposes their probabilistic limitations and the absence of domain-specific safeguards required for high-stakes surgical decision making. The findings underscore the urgent need for specialized Surgical-LLMs and Bio-LLMs-ethically aligned, clinically validated, and fine-tuned on high-quality operative, anatomical, and perioperative datasets. Future research should expand question complexity, include multi-turn dialog, and diversify evaluators. With responsible development, multimodal, privacy-preserving surgical LLM ecosystems could meaningfully augment pre-consultation education, risk communication, and patient support.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 .

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 .

This correspondence responds to the recent commentary on my article proposing a transparent, hybrid generative AI framework for patient selection in cosmetic surgery. The commentary rightly emphasizes the importance of explicit task specification, external and temporal validation, and clear threshold-to-action mapping to ensure safe and clinically meaningful deployment. I elaborate on how reasoning-capable large language models, specialty medical models, and retrieval-augmented generation pipelines can produce auditable, guideline-anchored suitability assessments, while acknowledging the need for stronger calibration, stratified reporting, and workflow-linked decision pathways. I also affirm the necessity of regulatory rigor, independent validation, privacy safeguards, and bias monitoring as prerequisites for real-world adoption. This exchange highlights a shared commitment to developing calibrated, ethical, and clinically respectful AI systems that enhance surgical judgment, protect patients, and support proportionate, evidence-aligned care in aesthetic practice.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 .

Background: Genioplasty and chin-augmentation are well-established procedures aimed at enhancing lower facial aesthetics. Traditionally, aesthetic outcomes have been assessed subjectively through expert opinions and patient-reported measures. The integration of artificial intelligence (AI) offers an objective approach to evaluating surgical results. This study utilizes the ICA Aesthetic Navigation AI Research Metrics Model (ICAAN® ARMM) to analyze postoperative changes in facial attractiveness, youthfulness, and skin quality following genioplasty.

Methods: Pre- and postoperative full-frontal images of 50 patients undergoing osseous genioplasty were analyzed using the ICAAN® ARMM. Therefore, an array of three aesthetic scores, the Facial Aesthetic Index (FAI), Facial Youthfulness Index (FYI), and Skin Quality Index (SQI), were measured before and after surgery, with subgroup analyses by age, sex, and ethnicity. Minimally clinically important differences (MCIDs) were estimated.

Results: All three aesthetic scores demonstrated improvement postoperatively, with FAI showing the greatest increase (82 (73-89) to 85 (75-92); p = 0.296), without showing statistical significance. Older patients (≥ 35 years) exhibited greater improvements in FAI scores compared to younger individuals (4 (1-10) vs. 1 (- 3-5); p = 0.028). Sex-related trends were observed, while lacking statistical significance. Ethnic subgroup analysis revealed no differences in score changes, suggesting cross-cultural applicability. Observed improvements did not exceed estimated MCIDs.

Conclusion: AI-assisted aesthetic analysis offers a novel, contemporary, and objective method for assessing genioplasty outcomes. While our study suggests general aesthetic improvements following surgery, further research incorporating larger data collections and subjective patient-reported measures is necessary. AI tools hold promise as a complementary tool in aesthetic medicine, supporting both clinicians and patients in surgical decision-making.

Level of evidence iii: 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 .

Background: Body Dysmorphic Disorder (BDD) is characterized by recurrent thoughts related to one or more perceived defects in physical appearance, which are either nonexistent or minimal to others, leading to emotional distress and impairments in social, occupational, and emotional functioning. The prevalence of BDD among individuals seeking plastic surgery is significantly higher than in the general population. It is therefore essential for plastic surgeons to identify individuals at greater risk for BDD. Several questionnaires are available for this purpose, typically administered by mental health professionals, with the Yale-Brown Obsessive-Compulsive Scale Modified for Body Dysmorphic Disorder (BDD-YBOCS) regarded as the gold standard. This study aimed to develop and validate a questionnaire designed for easy use by the attending physician, using the Brazilian Portuguese-translated BDD-YBOCS as a validation instrument.

Methods: This prospective cross-sectional study enrolled adults seeking primary or secondary facial plastic surgery at Hospital Paranaense of Otorhinolaryngology (January 2022-July 2024). Participants completed two questionnaires: a PILOT instrument developed from validated BDD tools and the BDD-YBOCS administered by a psychologist. Total scores were calculated, and thresholds defined to classify BDD probability.

Results: A total of 149 patients participated in the study, responding to both the PILOT questionnaire and the BDD-YBOCS. There was a strong correlation between the scores of the PILOT questionnaire and the BDD-YBOCS, with a Spearman's correlation coefficient of 0.79 (p < 0.001). The PILOT questionnaire demonstrated a sensitivity of 88.6% and a specificity of 77.2%. Internal consistency was excellent, with a Cronbach's alpha estimated at 0.91.

Conclusion: The PILOT questionnaire was validated successfully, proving to be a practical and reliable tool for use by attending physicians in identifying the possible presence or signs of BDD.

Level of evidence iii: 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 .