12. Preoperative Patient Guidance & Education In Aesthetic Breast Plastic Surgery: Assessment Of Artificial Intelligence Performance
Bibliographic record
Abstract
PURPOSE: 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 are on track to represent hallmark resources in this context. The authors aim to assess AI performance in answering questions posed by simulated patients interested in aesthetic breast plastic surgery. METHODS: ChatGPT was queried via 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; post-procedure instructions and recovery; and procedure cost and surgeon recommendations. Using standardized Likert scales, four expert breast plastic surgeons evaluated the AI responses. A post-participation survey assessed expert evaluators’ experience with AI, perceived utility, and limitations. RESULTS: 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 acknowledgement of limitations at 7.7/10±0.2. With regards to performance on procedures examined, ChatGPT’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. Score on breast implant-specific knowledge was 6.7/10±0.6. CONCLUSION: Albeit not without limitations, ChatGPT represents a promising resource for patient guidance and education. The model’s machine-learning capabilities may explain its improved performance efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".