Artificial Intelligence in Cosmetic Dermatology with Regard to Laser Treatments: A Comparative Analysis of AI and Dermatologists’ Decision-Making
Year: 2025
Type: article
Abstract: Introduction: Artificial intelligence (AI) has developed into an increasingly important tool in dermatology. While new technologies integrated within laser devices are emerging, there is a lack of data on the applicability of publicly available AI models. Methods: The prospective study used an online questionnaire where participants evaluated diagnosis and treatment for 25 dermatological cases shown as pictures. The same questions were given to AI models: ChatGPT-4o, Claude Sonnet 4, Gemini 2.5 Flash, and Grok-3. Results: Dermatologists outperformed AI in diagnostic accuracy (suspected primary diagnosis-SD 75.6%) in pooled dermatologists vs. pooled AI (SD 57.0%), with laser specialists achieving the highest accuracy (SD 82.0%) and residents the lowest (SD 66.0%). There was a high heterogeneity across AI models. Gemini approached dermatologist performance (SD 72.0%), while Claude showed a low accuracy (SD 40.0%). While AI models reached near 100% accuracy in some classic/common diagnoses (e.g., acne, rosacea, spider angioma, infantile hemangioma), their accuracy dropped to near 0% on rare or context-dependent cases (e.g., blue rubber bleb nevus syndrome, angiosarcoma, hirsutism, cutaneous siderosis). Inter-rater agreement was high among laser experts in terms of diagnostic accuracy and treatment choice. Agreement between residents and AI models was highest for diagnostic accuracy and treatment choice, while it was lowest between experts and AI models. Conclusions: Before AI-based tools can be integrated into daily practice, particularly regarding diagnosis and appropriate laser treatment recommendations, specific supervised medical training of the AI model is necessary, as open-source platforms currently lack the ability to contextualize presented data.
Source: Cosmetics
Authors: Alexandra Junge, Ali Mokhtari, Simone Cazzaniga, Ashraf Badawi, Flurin Brand, Simone Böll, Laurence Feldmeyer, Cindy Franklin, Hans-Joachim Laubach, Mathias Lehmann, Zora Martignoni, Sammy Murday, Dominik Obrist, Antonia Reimer-Taschenbrecker, Basil Signer, Roberta Vasconcelos-Berg, Charlotte Vogel, Nikhil Yawalkar, Kristine Heidemeyer, Seyed Morteza Seyed Jafari
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,172
OpenAlex ID: W7117470616