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Article Abstract
Background: To evaluate the performance of artificial intelligence (AI)-powered chatbots in generating treatment plans for facial aesthetic injections, focusing on their accuracy, safety, and clinical applicability.
Methods: A comparative observational study was conducted in an otolaryngology tertiary care department according to STROBE guidelines. Patients seeking facial injections were recruited from July to October 2024. Forty patients (85% female; mean age: 45.8 years) underwent photographic documentation and received AI-generated treatment plans for botulinum toxin and hyaluronic acid injections. Six AI chatbots and three generative vision models were evaluated based on five criteria: product selection, injection strategy, facial analysis, alignment with patient preferences, and safety. Likert scale ratings, each ranging from - 2 to + 2, were analyzed using Friedman and Durbin-Conover pairwise tests to identify significant differences (p < 0.05). The sum of the five Likert scales provided an overall score ranging from - 10 to + 10.
Results: ChatGPTo1 and ChatGPT4o achieved higher scores than other chatbots across most evaluation criteria, with mean total scores of 7.87 ± 0.29 and 7.85 ± 0.44, respectively (p = 0.295). Both chatbots were statistically superior (p < 0.05) to Claude, CopilotPro, and Llama in product selection (ChatGPT4o = 1.92 ± 0.05), injection strategy precision (ChatGPTo1 = 1.67 ± 0.08), alignment with patient preferences (ChatGPTo1 = 1.95 ± 0.03) and safety (ChatGPTo1 = 1.30 ± 0.17). Claude provided relevant facial analysis (1.50 ± 0.16) without significant difference compared to ChatGPT models (all p > 0.05). Generative vision models failed to produce relevant visual annotations.
Conclusion: Among the AI systems tested, ChatGPT-based chatbots demonstrated relatively superior performance in generating treatment plans for facial injections. However, safety limitations remain and preclude unsupervised clinical use.
Level Of Evidence Iv: 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 .
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