AI in Customized Dermatologic Care
Artificial intelligence (AI) is increasingly positioned as a transformative tool in dermatology, particularly in the development of customized skin care and clinical decision support. In a recent discussion, Renata Block, DMSc, MMS, PA-C, spoke with Nawar Shara, PhD, a leader in health data science and AI research, about the areas where these technologies add value, existing gaps, and necessary measures to ensure patient safety.
AI’s strength lies in its capacity to process vast volumes of data quickly and consistently. As Shara explains, machine learning models can evaluate thousands or even millions of images simultaneously, identify subtle patterns over time, and avoid human limitations such as fatigue. When appropriately designed and deployed, these tools can shift the clinician’s focus from repetitive visual comparisons to higher-level clinical judgment, patient communication, and care planning.
In the context of customized skin care, AI platforms incorporate clinical images, patient-reported data, environmental factors, and product information to generate individualized recommendations. For clinicians who frequently deal with acne, pigmentary disorders, inflammatory dermatoses, and photoaging, the potential for AI to support tailored interventions at scale is evident. However, both speakers discuss that not all AI solutions are created equal; the clinical utility heavily depends on how these systems are built and validated.
A predominant theme of the conversation is transparency. Trust in AI starts with a clear understanding of how a model was trained, the data sources utilized, and whether outcomes were clinically validated. Shara emphasizes the necessity for clinician involvement throughout development, not just during deployment. Tools crafted in isolation by technologists, without dermatology expertise, risk misalignment with actual clinical needs.
Bias is another critical concern, especially in dermatology, where skin tone, texture, and disease presentation vary widely. AI systems trained on narrow or non-representative datasets may perform well in specific demographics but underperform for patients with skin of color or less common conditions. Tackling these challenges requires intentional inclusion of diverse data and continuous performance monitoring, countering the assumption that algorithms are inherently objective.
Importantly, AI should be viewed not as a replacement for clinicians but as an augmentative tool. Education is imperative; dermatology providers must grasp both the strengths and limitations of AI to utilize it responsibly. Regulatory oversight, clear validation standards, and post-market surveillance are necessary to maintain patient safety as these tools transition from consumer-oriented skincare platforms to clinically adjacent applications.
The interview ultimately conveys a balanced message. AI carries significant promise for customized skin care and dermatologic practices, but its success hinges on transparency, data diversity, clinician engagement, and thoughtful integration into clinical workflows. In a field that relies heavily on visual assessment and longitudinal changes, AI could become a powerful ally if developed and employed with care.