A Familiar Story

When Mrs. M, a 38-year-old woman with deep brown skin, visited a dermatologist for laser resurfacing, she was excited to even out her skin tone and reduce fine lines. Her doctor assured her that the treatment was safe, but a few weeks later, she noticed dark patches forming where the laser had been applied—post-inflammatory hyperpigmentation (PIH), a common side effect in darker skin tones. Frustrated and self-conscious, she wished there had been a way to predict how her skin would react before undergoing the procedure.

Maya’s experience is not unique. For decades, aesthetic treatments have been optimized for lighter skin tones, leaving people with Fitzpatrick IV-VI skin at higher risk of complications. But what if technology could change this?

Why One-Size-Fits-All Aesthetics Doesn’t Work

Skin rejuvenation treatments—like laser resurfacing, microneedling, and chemical peels—rely on controlled injury to stimulate collagen and improve skin texture. However, in melanin-rich skin, inflammation can trigger an overproduction of pigment, leading to hyperpigmentation, hypopigmentation, or scarring¹. This challenge is understudied in dermatology research. Patients with darker skin tones, are often missing from trials studying light and laser therapies in dermatology². As a result, treatment guidelines are often based on data from lighter skin types, leaving dermatologists to rely on experience, rather than objective, data-driven insights.

The Role of AI in Personalized Skin Rejuvenation

Artificial intelligence (AI) has the potential to transform skin rejuvenation by predicting how an individual’s skin will respond to treatments before they even begin. Using machine learning (ML) algorithms trained on diverse datasets, AI could analyze factors like:

• Melanin levels to assess pigmentation risks
• Inflammation markers to predict healing response
• Genetic predispositions for scarring or hyperpigmentation
• Past treatment outcomes to refine future recommendations

By integrating AI-powered tools into dermatology, clinicians could personalize treatment settings—such as laser intensity, peel depth, or microneedling frequency and the entire course of treatment for safer, more effective outcomes in skin of color.

How it Works

At the core of AI-driven skin treatment personalization is predictive analytics, which uses historical patient data and real-time inputs to forecast treatment responses. These systems rely on supervised machine learning models, which are trained using labeled datasets containing past patient outcomes. For example, a deep learning algorithm could be trained on thousands of cases where patients with darker skin underwent laser treatments. By analyzing patterns in outcomes, the model can recognize which skin types and conditions are most prone to PIH or scarring. Additionally, AI-powered risk stratification can categorize patients based on their likelihood of adverse effects. Natural language processing (NLP) can even analyze electronic health records (EHRs) and patient-reported symptoms to refine predictions further. Over time, these models become more accurate, offering dermatologists a real-time risk assessment tool before treatment begins.

Why This Matters

For decades, skin of color has been overlooked in aesthetic research. AI can help close this gap by:

• Creating safer, data-driven treatment protocols
• Minimizing risks of PIH, scarring, and ineffective treatments
• Empowering dermatologists with objective, predictive insights

The Future of AI in Aesthetic Medicine

The next step? Expanding AI models with more diverse datasets to ensure precision in skincare recommendations, energy-based treatments, and post-procedure healing predictions.

Imagine a future where Mrs. M —and anyone with skin of color—can confidently undergo a rejuvenation treatment knowing that AI has already identified the safest, most effective approach.

Full Circle: A Different Outcome for Maya

If Maya had access to AI-powered treatment planning, she might have received a customized plan—perhaps recommending a lower-intensity laser setting, a gentler peel, or an alternative treatment like radiofrequency microneedling, which carries a lower risk of PIH. Instead of dealing with months of hyperpigmentation, she would have achieved smoother, more even skin safely.

The future of aesthetic medicine isn’t just about looking better—it’s about ensuring safer, more inclusive care for everyone.

References
1. Davis EC, Callender VD. Aesthetic dermatology for aging ethnic skin. Dermatologic Surgery. 2011 Jun 28;37(7):901-17.
2. Manjaly P, Xia E, Allan A, et al. Skin phototype of participants in laser and light treatments of cosmetic dermatologic conditions: A systematic review. Jour of Cosm Dermatol. 2023. doi:10.1111/jocd.15739
3. Bencevic M, Habijan M, Gali? I, Babin D, Pizurica A. Understanding skin color bias in deep learning-based skin lesion segmentation. Computer methods and programs in biomedicine. 2024 Mar 1;245:108044.