Evaluation of Aesthetic Outcomes Following Botulinum Toxin Treatment Using Multimodal Large Language Models: A Paired Before-and-After Analysis.

BACKGROUND

Multimodal large language models (MLLMs) are increasingly applied to visual assessment tasks, yet their ability to evaluate aesthetic treatment outcomes remains unclear.

OBJECTIVES

The aim of this study was to assess whether contemporary MLLMs can identify treatment state and detect region-specific aesthetic improvement following botulinum neurotoxin treatment.

METHODS

In this observational study, 23 paired facial image cases (46 images; 460 model evaluations) were analyzed. Four MLLMs (GPT-5.4 Pro [OpenAI, San Francisco, CA], Grok 4.1 [xAI, San Francisco, CA], Gemini 3.1 Pro [Google DeepMind, Mountain View, CA], and Claude Opus 4.6 [Anthropic, San Francisco, CA]) performed 5 independent inference runs per case. Models identified the posttreatment image and assessed regional improvement (forehead, glabella, and periorbital). Accuracy, sensitivity, specificity, balanced accuracy, Matthews correlation coefficient, and Fleiss' κ were calculated descriptively. Performance was compared with majority-class baselines. Exploratory outputs included aesthetic scores and apparent age estimates.

RESULTS

All models identified the posttreatment image (100% accuracy). Region-specific improvement detection frequently failed to exceed majority-class baselines (65.2%-91.3%). Gemini 3.1 Pro showed the highest performance for the forehead (74.8%) and glabella (63.5%), whereas no model reached the periorbital baseline. Inter-run reliability varied widely (κ -0.113 to 0.719). High reliability did not imply correctness. All models systematically overestimated improvement (62.6%-94.8% of predictions exceeded ground truth). False positives exceeded false negatives in all 12 model-task combinations. Exploratory outputs indicated perceived rejuvenation.

CONCLUSIONS

MLLMs recognize the format of aesthetic change but not its clinical nuance. Bridging this gap requires more than improved accuracy: a coordinated agenda of domain-specific fine-tuning, expert-rater benchmarking, and structured outcome frameworks, supported by governance of training-data provenance and clear safeguards before any clinical or research deployment.

LEVEL OF EVIDENCE 5 THERAPEUTIC: For image description, please refer to the figure legend and surrounding text.