Leveraging ChatGPT for Enhanced Aesthetic Evaluations in Minimally Invasive Facial Procedures.

BACKGROUND

In recent years, the application of AI technologies like ChatGPT has gained traction in the field of plastic surgery. AI models can analyze pre- and post-treatment images to offer insights into the effectiveness of cosmetic procedures. This technological advancement enables rapid, objective evaluations that can complement traditional assessment methods, providing a more comprehensive understanding of treatment outcomes.

OBJECTIVE

The study aimed to comprehensively assess the effectiveness of custom ChatGPT model, "Face Rating and Review AI," in facial feature evaluation in minimally invasive aesthetic procedures, particularly before and after Botox treatments.

METHOD: An analysis was conducted on the Web of Science (WoS) database, identifying 79 articles published between 2023 and 2024 on ChatGPT in the field of plastic surgery from various countries. A dataset of 23 patients from Kaggle, including pre- and post-Botox images, was used. The custom ChatGPT model, "Face Rating & Review AI," was used to assess facial features based on objective parameters such as the golden ratio, symmetry, proportion, side angles, skin condition, and overall harmony, as well as subjective parameters like personality, temperament, and social attraction.

RESULT: The WoS search found 79 articles on ChatGPT in plastic surgery from 27 countries, with most publications originating from the USA, Australia, and Italy. The objective and subjective parameters were analyzed using a paired t-test, and all facial features showed low p-values (<0.05). Higher mean scores on features such as the golden ratio (mean = 5.86, SD = 0.69), skin condition (mean = 3.78, SD = 0.73), and personality (mean = 5.0, SD = 0.79) indicate positive shifts after the treatment.

CONCLUSION

The custom ChatGPT model "Face Rating and Review AI" is a valuable tool for assessing facial features in Botox treatments. It effectively evaluates objective and subjective attributes, aiding clinical decision-making. However, ethical considerations highlight the need for diverse datasets in future research to improve accuracy and inclusivity.

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