The Role of AI in Enhancing Cosmetic Dermatology Practices

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Cosmetic dermatology is an evolving field that enhances skin appearance, addressing issues like wrinkles, photodamage, skin laxity, and pigmentation. This specialty combines scientific knowledge with artistic techniques to achieve desired aesthetic results. However, success in cosmetic interventions is often subjective, influenced by individual perception, beauty standards, and manual observation. As a result, there is increasing interest in the potential role of artificial intelligence (AI) to address these subjective challenges in cosmetic dermatology.A recent review analyzed research on AI in cosmetic dermatology to understand the efficacy, satisfaction, and outcomes of the tool within this specialty.¹

Methods

A comprehensive search was conducted on PubMed to gather all the published material related to the current implications of AI in medicine, specifically within the field of dermatology. Researchers identified 53 articles related to the subject, 33 of which were selected to be included in the review.

Results

The articles included in the review illustrated various facets of AI within the field, including:

• AI in cosmetic dermatology: In medicine, AI's use is emerging, with a 2023 AMA survey showing 38% of physicians employing AI for tasks like creating care plans, documentation, translation services, and assistive diagnosis.² In dermatology, AI has proven effective in diagnosing and classifying melanoma more accurately than dermatologists.³ As cosmetic dermatology focuses on enhancing skin appearance and addressing aesthetic concerns, integrating AI can provide a more objective and personalized approach. AI can analyze images to identify fine lines and suggest optimal treatments, reduce human error, and support data-driven decision-making to tailor treatments to individual patient needs and expectations.⁴
• AI in cosmetic consultations and assessing outcomes: AI technology is revolutionizing this process by offering quick, objective analyses that enhance both efficiency and accuracy. For instance, AI-driven image recognition tools, such as DenseNet201, improve skin quality assessments by providing noninvasive, accurate measurements of hydration and skin conditions.⁵ Additionally, AI systems like ResNets and CNNs offer precise sebum production assessments, moving beyond subjective manual observations.⁶ Researchers noted AI advancements in measuring skin thickness and collagen levels non-invasively eliminate the need for biopsies. Furthermore, AI excels in identifying and classifying skin lesions and pigmentation, aiding in treatment selection and achieving diagnostic accuracy comparable to expert dermatologists.⁷ 
• AI in treatment prediction and progress: AI models, such as artificial neural networks, can forecast the number of sessions required for treatments like Excimer laser, which can be applied to other cosmetic procedures for conditions like dyschromia and acne scarring.⁴ By recommending treatments and predicting their efficacy, researchers found AI can help patients choose the most suitable options, potentially reducing the need for multiple sessions and helping to manage costs.⁸ Additionally, AI enables the creation of precise 3D facial models to determine the exact amount of dermal filler needed, offering better planning for both practitioners and patients.⁹ The review stated AI also improves real-time tracking of treatment progress, surpassing traditional subjective methods.
• AI in patient education: AI is increasingly being used as an interactive tool to help patients maintain their cosmetic results post-treatment. For instance, researchers behind 1 study developed a “skincare mirror” that simulates potential outcomes from using specific skincare products, allowing users to make more informed decisions and explore products more efficiently. This system was particularly well-received by users with limited skincare knowledge, highlighting AI's role in enhancing engagement and satisfaction.¹⁰ Additionally, AI-driven tools like Huang et al.'s Alluring system analyze various skin factors to provide personalized product recommendations, while Liu et al. integrated genetic data with cosmetic product information to further tailor recommendations.^10-11

Conclusion

The review found the integration of AI in cosmetic dermatology holds promise by enabling the analysis of extensive datasets and providing more personalized patient experiences. Researchers behind this review believe that AI can complement the artistic expertise of cosmetic dermatologists, enhancing evidence-based decision-making while preserving the crucial human touch in the field. By finding a balance between AI and human expertise, this technology has the potential to transform various aspects of cosmetic dermatology, including consultations, treatment planning, progress analysis, and patient education.

While AI offers significant benefits, the review noted it also faces limitations, particularly regarding data quality and inherent biases. AI systems depend on accurate and comprehensive data sets; errors or imbalances in these data can lead to unreliable outputs. Additionally, AI can inherit biases from its training data, which can result in unfair outcomes, such as favoring certain skin types or features. Researchers found ethical and regulatory concerns also arise, particularly concerning patient privacy and data security, as clinical imaging data used in AI systems can be identifiable and sensitive. They stated standardizing image capturing and storage practices and protecting user confidentiality are essential for integrating AI into healthcare. The review also suggested that incorporating AI education into medical school curriculums can help future healthcare professionals effectively utilize this technology while addressing these challenges.

References

1. Kania B, Montecinos K, Goldberg DJ. Artificial intelligence in cosmetic dermatology. J Cosmet Dermatol. 2024; 00: 1-7. doi:10.1111/jocd.16538
2. AMA: Physicians enthusiastic but cautious about health care AI. Press Release. American Medical Association. Published December 14, 2023. Accessed August 28, 2024. https://www.ama-assn.org/press-center/press-releases/ama-physicians-enthusiastic-cautious-about-health-care-ai.
3. Brinker TJ, Hekler A, Enk AH, et al. Deep neural networks are superior to dermatologists in melanoma image classification. Eur J Cancer. 2019;119:11-17. doi:10.1016/j.ejca.2019.05.023
4. Elder A, Ring C, Heitmiller K, et al. The role of artificial intelligence in cosmetic dermatology-Current, upcoming, and future trends. J Cosmet Dermatol. 2021;20(1):48-52. doi:10.1111/jocd.13797
5. Chirikhina E, Chirikhin A, Dewsbury-Ennis S, et al. Skin characterizations by using contact capacitive imaging and high-resolution ultrasound imaging with machine learning algorithms. Appl Sci. 2021; 11(18): 8714. doi: 10.3390/app11188714
6. Borade S, Kalbande D, Pereira K, et al. Deep scattering convolutional network for cosmetic skin classification. Int J Eng Trends Technol. 2022; 70(7): 10-23.
7. Hu F, Santagostino SF, Danilenko DM, et al. Assessment of skin toxicity in an in vitro reconstituted human epidermis model using deep learning. Am J Pathol. 2022;192(4):687-700. doi:10.1016/j.ajpath.2021.12.007
8. Georgievskaya A. Artificial intelligence confirming treatment success: The role of gender- and age-specific scales in performance evaluation. Plast Reconstr Surg. 2022;150(4 Suppl ):34S-40S. doi:10.1097/PRS.0000000000009671
9. Ali Shah SA, Bennamoun M, Molton M. A fully automatic framework for prediction of 3D facial rejuvenation. 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE; 2018.
10. Huang W, Hong B, Cheng W, et al. A cloud-based intelligent skin and scalp analysis system. 2018 IEEE Visual Communications and Image Processing (VCIP). IEEE; 2018.
11. Liu X, Chen CH, Karvela M, et al. A DNA-based intelligent expert system for personalized skin-health recommendations. IEEE J Biomed Health Inform. 2020;24(11):3276-3284. doi:10.1109/JBHI.2020.2978667