From Skin Images to Treatment: AI Enters Dermatology Clinics

Artificial intelligence is no longer limited to skincare apps that recommend cleansers, moisturizers or cosmetic products. AI-powered tools are increasingly entering dermatology clinics, where they can assist physicians in analyzing skin images, identifying relevant patterns and comparing treatment options that may be suitable for each patient.

Specialized systems can examine enlarged images of moles, pigmentation, rashes, wounds and other skin changes, then compare them with large collections of medical images and clinical data. The technology looks for patterns that may indicate a particular condition or highlight a change requiring further examination.

The potential benefit extends beyond suggesting possible diagnoses. Some systems can combine image findings with information such as the patient’s age, skin tone, medical history and current medications, helping dermatologists assess available treatment alternatives and reduce the risk of choosing an unsuitable option.

However, AI-generated results remain a form of clinical support rather than a final medical decision. The dermatologist must still examine the patient, consider their complete medical history and determine whether the suggested diagnosis or treatment is appropriate.

This type of assistance may be particularly useful in cases involving acne, eczema, psoriasis, chronic wounds, pigmentation disorders and hair loss. Responses to treatment often vary from one patient to another, and doctors may need to adjust dosages, combine therapies or monitor the condition over several weeks or months.

AI can also compare skin images taken during different medical appointments and measure changes in inflammation, wound size or pigmentation. This may provide doctors with more consistent information about whether a treatment is working instead of relying solely on visual observation.

In skin cancer care, researchers are developing systems that do more than provide a brief prediction. Newer tools may also explain the visual features behind their assessment, including irregular mole borders, colour variation or unusual patterns within a skin lesion.

A 2025 study published in Nature Communications, involving 76 dermatologists, found that explainable artificial intelligence improved doctors’ average balanced diagnostic accuracy by approximately 2.8 percentage points compared with an AI system that offered predictions without explanations.

The study reported that diagnostic accuracy increased from 79.9% with conventional AI support to 82.7% when physicians used an explainable system.

The results highlight the importance of preventing artificial intelligence from becoming a “black box” that produces decisions without showing how they were reached. Doctors need to understand the reasoning behind a recommendation so they can accept, reject or compare it with physical examinations and laboratory findings.

These technologies may also support healthcare systems in regions facing shortages of dermatologists. AI-assisted tools could help family physicians assess cases and identify patients who require an urgent specialist referral, while allowing less serious conditions to be safely monitored within primary care.

The growing use of AI in skincare also carries significant risks, particularly as consumer applications increasingly ask users to photograph a mole, rash or skin mark and then provide an immediate diagnosis or treatment recommendation without direct medical supervision.

The American Academy of Dermatology warns against relying on such applications for diagnosis or treatment. An inaccurate result could delay the detection of skin cancer or encourage the use of a product or medication that makes the condition worse.

Accuracy may also decline when an application’s training data does not include enough images representing different skin tones or uncommon medical conditions.

Skin-tone diversity remains one of the most important challenges facing dermatology AI. Redness, inflammation and some signs of skin cancer can appear differently on darker skin than on lighter skin. Safe systems must therefore be trained and tested using diverse patient populations.

Privacy is another major concern because photographs of the face and skin, together with medical records, are sensitive health information. Patients should know where their images are stored, who can access them and whether the information may later be used to train other AI systems.

Artificial intelligence is unlikely to replace dermatologists, but it may change how they work. Technology can rapidly examine thousands of images and possible patterns, while the physician remains responsible for listening to the patient, examining the skin, hair and nails, and balancing the potential benefits and risks of treatment.

As AI becomes a new digital skincare assistant, the safest model remains a partnership between human medical expertise and technological speed—not handing control of a patient’s health to an unsupervised mobile application.