Use of Text-to-Image Artificial Intelligence Model in Preoperative Counseling for Lip-Lift Procedures
Bibliographic record
Abstract
SUMMARY: Text-to-image models powered by artificial intelligence offer a promising tool for enhancing patients' comprehension of cosmetic surgery outcomes and providing personalized visual forecasts of their appearance after the procedure. This study explores the efficacy of text-to-image AI models, specifically DALL·E2, in improving preoperative counseling for patients undergoing lip-lift procedures. Preoperative photographs of 4 patients, who had given their consent, were processed using DALL·E2, which allows users to modify specific areas of an image and input text descriptions to visualize anticipated changes. The authors successfully demonstrated the ability of DALL·E2 to generate accurate visual predictions within a short timeframe of 2 minutes. Selected images provided realistic expectations of the postoperative appearance, aiding in better patient understanding and expectation management.
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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 | 0.000 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".