10 Ways AI is Being Used in Dermatology [+5 Case Studies][2026]

Artificial Intelligence (AI) is rapidly transforming dermatology, bringing precision, scalability, and accessibility to skin care like never before. From diagnosing skin conditions to monitoring chronic diseases and optimizing clinical workflows, AI is enabling faster and more accurate decision-making across the dermatology ecosystem. Healthcare providers are now leveraging advanced algorithms, computer vision, and data analytics to enhance patient outcomes while reducing the burden on specialists. Real-world implementations—from autonomous lesion triage systems to AI-driven psoriasis and atopic dermatitis assessment tools—demonstrate how technology is reshaping both clinical and remote care. These innovations are not only improving efficiency but also empowering patients with better self-monitoring capabilities. In this article, DigitalDefynd explores how AI is being applied in dermatology through practical, real-world case studies, highlighting its growing impact on diagnosis, treatment, and long-term disease management across diverse dermatological conditions.

 

Use of AI in Dermatology: 5 Case Studies

1. Chelsea and Westminster Hospital NHS Foundation Trust: Use of autonomous AI to triage and discharge benign skin lesions

Challenge

Chelsea and Westminster Hospital NHS Foundation Trust faced a growing dermatology backlog driven by increasing referrals for suspected skin cancer, many of which turned out to be benign lesions. Dermatology departments across the UK were under pressure, with waiting times often exceeding NHS targets and specialist capacity stretched thin. Approximately 50–60% of urgent skin cancer referrals were found to be non-cancerous, leading to inefficient use of consultant time and delayed care for high-risk patients. The hospital needed a scalable solution to improve triage efficiency, reduce unnecessary face-to-face consultations, and accelerate diagnosis without compromising patient safety or clinical accuracy.

 

Solution

a. Autonomous AI Deployment: The hospital implemented Skin Analytics’ DERM AI system, one of the first autonomous AI tools approved for clinical use in dermatology. This system analyzes dermoscopic images of skin lesions and provides immediate diagnostic recommendations without requiring clinician intervention for low-risk cases.

b. Clinical Workflow Integration: The AI solution was embedded into the hospital’s existing dermatology pathway, allowing patients to have lesions imaged by trained staff. The AI then categorized lesions into risk levels, enabling faster triage decisions and reducing dependency on dermatologists for routine assessments.

c. High-Accuracy Classification: The system demonstrated sensitivity rates exceeding 97% for detecting malignant lesions, ensuring that high-risk cases were prioritized for specialist review while safely discharging benign cases. This high accuracy helped build clinical trust and supported regulatory approval.

d. Automated Discharge of Benign Cases: Patients identified with low-risk lesions were automatically discharged without needing a dermatologist consultation. This significantly reduced the number of unnecessary follow-ups and optimized resource allocation within the department.

e. Scalability Across NHS Pathways: The deployment served as a model for broader NHS adoption, showcasing how AI could manage high patient volumes. The system was designed to scale across multiple sites, supporting national efforts to reduce dermatology backlogs.

f. Improved Patient Experience: Patients received faster results, often within the same visit, reducing anxiety associated with long waiting times. The streamlined process also minimized hospital visits, improving convenience and overall satisfaction.

 

Result

The implementation of autonomous AI led to a significant reduction in dermatology workload, with up to 40% of patients safely discharged without needing a specialist consultation. Waiting times for urgent cases improved as dermatologists could focus on high-risk patients, enhancing overall care quality. The system demonstrated real-world safety and efficiency, supporting faster diagnosis while maintaining high clinical accuracy. This initiative positioned Chelsea and Westminster Hospital as a pioneer in AI-driven dermatology, proving that autonomous AI can effectively transform clinical workflows at scale.

 

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2. SkinTeller: AI-based psoriasis severity assessment tested in real-world clinical practice

Challenge

Psoriasis is a chronic inflammatory skin condition affecting over 125 million people globally, requiring continuous monitoring and accurate severity assessment for effective treatment. Traditionally, clinicians rely on scoring systems such as the Psoriasis Area and Severity Index (PASI), which can be subjective, time-consuming, and prone to inter-observer variability. In busy clinical settings, consistent and frequent assessment becomes difficult, leading to delays in treatment optimization. Moreover, patients often lack tools for reliable self-monitoring between visits, resulting in gaps in disease tracking. There was a clear need for a scalable, objective, and easy-to-use solution that could standardize psoriasis assessment both in clinics and remotely.

 

Solution

a. AI-Powered Image Analysis: SkinTeller utilizes deep learning algorithms to analyze patient-captured skin images and automatically assess psoriasis severity, reducing reliance on manual scoring systems.

b. Standardized Severity Scoring: The platform replicates clinical scoring metrics such as PASI by evaluating lesion characteristics, including redness, scaling, and thickness, ensuring consistent and objective assessments across users.

c. Smartphone-Based Accessibility: Patients and clinicians can capture images using standard smartphone cameras, eliminating the need for specialized equipment and enabling widespread adoption in real-world settings.

d. Real-Time Feedback: The system provides near-instant severity assessments, allowing clinicians to make quicker treatment decisions and patients to better understand their disease progression.

e. Remote Monitoring Capabilities: SkinTeller supports teledermatology by enabling patients to regularly upload images from home, facilitating continuous disease tracking without frequent in-person visits.

f. Clinical Validation: In real-world studies, SkinTeller demonstrated strong correlation with dermatologist assessments, achieving high agreement rates and improving consistency in severity evaluation.

g. Data-Driven Insights: Aggregated patient data allows clinicians to identify treatment patterns, monitor response over time, and personalize therapy based on objective metrics.

 

Result

SkinTeller significantly improved the accuracy and consistency of psoriasis severity assessment, with studies showing strong agreement with clinician-evaluated scores and reduced variability. The tool enabled more frequent and accessible monitoring, leading to better-informed treatment adjustments and improved patient outcomes. By reducing the burden on clinicians and empowering patients with self-assessment capabilities, SkinTeller enhanced both clinical efficiency and patient engagement, demonstrating the value of AI in managing chronic dermatological conditions at scale.

 

3. DERMACLEAR: Using AI to extract real-world evidence from EHRs for hidradenitis suppurativa, chronic urticaria, psoriasis, and atopic dermatitis

Challenge

Chronic inflammatory skin diseases such as hidradenitis suppurativa, chronic urticaria, psoriasis, and atopic dermatitis affect millions of patients and require long-term management. However, understanding real-world disease burden, treatment effectiveness, and patient outcomes remains challenging due to fragmented and unstructured electronic health record (EHR) data. Much of the critical clinical information is stored in free-text notes, making it difficult to extract insights using traditional methods. This lack of structured data limits large-scale research, delays evidence generation, and hampers data-driven decision-making in dermatology. There was a need for advanced tools to unlock meaningful insights from vast, complex healthcare datasets.

 

Solution

a. Natural Language Processing (NLP): DERMACLEAR leverages advanced NLP algorithms to analyze unstructured clinical notes within EHRs, extracting relevant dermatology-specific information such as symptoms, diagnoses, and treatment responses.

b. Automated Data Structuring: The platform converts free-text data into structured formats, enabling large-scale analysis of patient records across multiple conditions and healthcare systems.

c. Multi-Disease Coverage: DERMACLEAR is designed to handle multiple dermatological conditions simultaneously, providing insights into diseases like hidradenitis suppurativa, chronic urticaria, psoriasis, and atopic dermatitis.

d. Real-World Evidence Generation: The system identifies patterns in treatment outcomes, disease progression, and patient demographics, supporting evidence-based clinical and policy decisions.

e. Scalable Analytics: AI-driven processing allows analysis of thousands of patient records efficiently, significantly reducing the time and cost compared to manual data extraction methods.

f. Clinical Decision Support: Insights generated from real-world data help clinicians refine treatment strategies and better understand patient responses to therapies in routine practice.

g. Research Acceleration: The platform supports clinical research by providing high-quality datasets that can be used for epidemiological studies, drug development, and healthcare optimization.

 

Result

DERMACLEAR enabled the extraction of large-scale, high-quality real-world evidence from previously inaccessible EHR data, significantly improving the understanding of chronic dermatological diseases. The platform accelerated research timelines and provided actionable insights into treatment effectiveness and disease burden. By transforming unstructured data into meaningful information, DERMACLEAR enhanced clinical decision-making and supported more personalized and data-driven patient care, demonstrating the powerful role of AI in advancing dermatology research and healthcare analytics.

 

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4. Keio University School of Medicine: Using AI and smartphone photos to assess atopic dermatitis severity in real-world patient settings

Challenge

Atopic dermatitis (AD) is a chronic inflammatory skin condition affecting nearly 10–20% of children and 2–10% of adults worldwide, requiring regular monitoring to manage flare-ups and treatment effectiveness. Traditional severity scoring systems, such as Eczema Area and Severity Index (EASI), depend heavily on in-clinic evaluations by dermatologists, making frequent assessments impractical. Patients often experience fluctuations in symptoms between visits, but lack reliable tools to document and communicate these changes. Additionally, subjective variability in clinician assessments and limited access to dermatology specialists further complicate disease management. There was a pressing need for an objective, accessible, and scalable solution to enable continuous and accurate monitoring of atopic dermatitis outside clinical environments.

 

Solution

a. Smartphone-Based Image Capture: The Keio University team developed an AI system that allows patients to capture images of affected skin areas using standard smartphone cameras, enabling easy and frequent monitoring from home.

b. Deep Learning for Severity Assessment: The AI model uses advanced convolutional neural networks (CNNs) trained on large dermatology image datasets to evaluate key indicators such as redness, swelling, and lesion distribution.

c. Automated EASI Scoring Approximation: The system replicates components of the EASI scoring method by quantifying severity across different body regions, ensuring alignment with clinical standards.

d. Real-World Patient Integration: The tool was tested in real-world settings, allowing patients to submit images remotely, which were then analyzed to provide objective severity scores without requiring clinic visits.

e. Consistency and Objectivity: By standardizing image-based evaluation, the AI reduces inter-observer variability commonly seen in manual assessments, improving reliability in disease tracking.

f. Teledermatology Enablement: The platform supports remote consultations by providing dermatologists with structured and quantitative data, enhancing decision-making during virtual visits.

g. Continuous Monitoring and Alerts: Patients can track changes over time, and the system can flag worsening conditions, enabling timely medical intervention and improved disease control.

 

Result

The AI system demonstrated strong correlation with dermatologist assessments, showing high agreement with traditional scoring methods while significantly reducing variability. Patients were able to monitor their condition more frequently, leading to better disease management and earlier detection of flare-ups. The solution improved access to care, particularly for patients in remote or underserved areas, while reducing the need for frequent in-person visits. By combining accessibility with clinical accuracy, Keio University’s approach highlighted the potential of AI-driven tools to transform chronic disease management in dermatology.

 

5. Western Switzerland Multicenter Study: Developing an AI-powered mobile tool for chronic wound assessment and monitoring

Challenge

Chronic wounds, including diabetic ulcers, pressure sores, and venous leg ulcers, affect millions of patients globally and impose a significant burden on healthcare systems. Accurate wound assessment is critical for effective treatment, but traditional methods rely on manual measurements and visual inspection, which can be inconsistent and prone to error. Clinicians often face challenges in tracking wound progression over time, especially in outpatient or home-care settings. Additionally, frequent clinic visits for monitoring increase costs and inconvenience for patients. With chronic wounds accounting for billions in annual healthcare expenditures, there was a clear need for a reliable, scalable, and remote solution to improve wound evaluation and management.

 

Solution

a. AI-Based Image Analysis: The multicenter study developed a mobile application that uses AI algorithms to analyze wound images and automatically measure key parameters such as size, depth, and tissue composition.

b. Standardized Measurement Techniques: The system replaces manual ruler-based measurements with precise digital calculations, improving consistency and reducing human error in wound assessment.

c. Smartphone Integration: Healthcare providers and patients can capture wound images using smartphones, enabling easy data collection in both clinical and home-care environments.

d. Longitudinal Tracking: The tool stores historical data and visual records, allowing clinicians to monitor wound healing progression over time and adjust treatments accordingly.

e. Tissue Classification Capabilities: AI models classify different tissue types (e.g., granulation, necrotic tissue), providing deeper clinical insights into wound status and healing stages.

f. Remote Care Enablement: The platform supports telemedicine by allowing clinicians to assess wounds remotely, reducing the need for frequent in-person visits and improving patient convenience.

g. Multicenter Validation: The system was tested across multiple healthcare centers in Western Switzerland, demonstrating scalability and robustness in diverse clinical settings.

 

Result

The AI-powered tool significantly improved the accuracy and consistency of wound assessments, reducing variability compared to traditional methods. Clinicians were able to track healing progress more effectively, leading to better-informed treatment decisions and improved patient outcomes. The ability to conduct remote assessments reduced healthcare costs and patient burden while maintaining high-quality care. This multicenter study demonstrated that AI-driven mobile solutions can transform chronic wound management by enhancing precision, efficiency, and accessibility in dermatological care.

 

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10 Ways AI is Being Used in Dermatology

1. Skin Cancer Detection

AI algorithms, particularly those from companies like Google Health and IBM Watson Health, are transforming skin cancer detection. Google Health’s AI system can analyze dermoscopic images of skin lesions with remarkable accuracy, often matching or surpassing the diagnostic abilities of dermatologists. This AI is trained on extensive datasets of skin lesion images, enabling it to identify patterns indicative of melanoma and other skin cancers. IBM Watson Health also employs AI for early detection of skin cancers, leveraging its vast medical database and machine learning capabilities to improve diagnostic accuracy. These AI tools are particularly valuable in early detection, where timely intervention can significantly increase survival rates. For example, Google Health’s algorithm has demonstrated over 90% accuracy in identifying malignant lesions. These advancements not only enhance diagnostic precision but also help in managing the workload of dermatologists, allowing for quicker and more accurate assessments.

 

2. Automated Diagnosis

Automated diagnosis through AI platforms like SkinVision is revolutionizing how skin conditions are assessed. SkinVision’s app allows users to take photos of their skin conditions, which are then analyzed by AI algorithms trained on millions of dermatological images. The app provides an initial risk assessment, categorizing the condition as low, medium, or high risk. This preliminary diagnosis can prompt users to seek professional medical advice sooner, potentially catching serious conditions early. Another example is First Derm, an app that uses AI to provide quick and reliable skin condition assessments. These tools democratize access to dermatological insights, especially in places with limited access to healthcare professionals. By decreasing the requirement for in-person consultations for minor issues, AI-driven automated diagnosis systems can also help alleviate the burden on healthcare systems, ensuring that dermatologists can focus on more complex cases.

 

3. Personalized Treatment Plans

Personalized skincare is becoming more accessible with AI-driven apps like L’Oreal’s SkinConsultAI. This app uses advanced machine learning to analyze selfies, examining skin type, age, and lifestyle factors to recommend customized skincare routines. The AI is trained on a vast dataset of skin images and clinical research, allowing it to provide personalized advice based on individual skin needs. SkinConsultAI can suggest products from L’Oreal’s extensive skincare range, ensuring users receive recommendations tailored to their unique skin concerns. Another notable example is Curology, which uses AI to create bespoke skincare formulas. Users complete a questionnaire and submit photos, which the AI analyzes to formulate a personalized treatment plan. These AI-driven solutions not only enhance the effectiveness of skincare routines but also educate users about their skin’s specific needs, leading to better long-term skin health and satisfaction.

 

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4. Remote Consultations

Teledermatology is gaining momentum with AI tools like DermTech, which facilitates remote consultations. Patients can upload photos of their skin conditions through the DermTech app, where an AI algorithm performs an initial analysis. This preliminary assessment helps dermatologists prioritize cases based on urgency. DermTech’s AI can detect various skin conditions, including melanoma, by analyzing biomarkers and patterns in the images. This remote diagnostic capability is particularly beneficial in areas with limited access to dermatologists, providing timely and accurate assessments.

Another example is the app SkinIO, which offers remote skin monitoring and consultations. These AI-powered platforms streamline the diagnostic process, reduce wait times for consultations, and make dermatological care more accessible. By enabling remote monitoring and follow-ups, these tools also help in the ongoing management of chronic skin conditions, ensuring that patients receive continuous and consistent care.

 

5. Acne Management

AI is playing a crucial role in acne management, with brands like Neutrogena leading the way. Neutrogena’s Skin360 app uses AI to analyze user selfies, assessing factors such as acne severity, skin texture, and overall skin health. On the basis of this analysis, the app provides personalized skincare recommendations, including specific products and routines to address acne. The AI algorithms are trained on an expanded dataset of skin images, enabling precise assessment and tailored advice. Another example is the brand MDacne, which uses AI to create customized acne treatment plans. Users answer a few questions and submit photos, and the AI formulates a regimen that includes cleansers, treatments, and moisturizers specifically suited to their skin type and acne condition. These AI-driven solutions help users achieve better acne control by providing targeted and effective treatments, reducing the trial-and-error process typically associated with acne management.

 

6. Psoriasis Monitoring

Managing chronic skin conditions like psoriasis is becoming more efficient with AI tools such as Miiskin. The Miiskin app allows users to photograph their skin lesions regularly, creating a visual diary of their condition over time. AI algorithms analyze these images to detect changes and track the progression of psoriasis, providing valuable insights into treatment effectiveness. This continuous monitoring helps patients and dermatologists make informed decisions about adjusting treatments. Another example is the use of AI in the Clarify Medical Home Light Therapy System, which uses AI to personalize phototherapy treatment for psoriasis. The AI adjusts the dosage and frequency of light therapy based on the patient’s response, optimizing treatment outcomes. These AI-driven tools empower patients to manage their psoriasis actively, leading to better adherence to treatment plans and improved quality of life.

 

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7. Anti-Aging Solutions

AI is enhancing anti-aging skincare with solutions like Olay’s Skin Advisor. This app uses AI to analyze selfies, assessing factors such as wrinkles, fine lines, and skin texture to provide personalized anti-aging skincare advice. The AI recommends products from Olay’s range that are best suited to address specific aging concerns identified in the analysis. The app’s recommendations are based on a comprehensive understanding of skin aging patterns, thanks to its training on a large dataset of diverse skin images. Another example is the brand Foreo, which uses AI in its beauty devices to analyze skin and provide tailored skincare routines. These AI-powered solutions help users tackle aging signs more effectively by offering precise and personalized advice, improving skincare outcomes, and promoting healthier, youthful-looking skin.

 

8. Eczema Management

AI is revolutionizing eczema management through platforms like YoDerm. This telemedicine service uses AI to analyze patient photos and medical histories, creating personalized treatment plans for eczema sufferers. The AI considers factors such as the severity of eczema, skin type, and previous treatment responses to suggest optimal skincare routines and topical treatments. Another notable example is the AI-driven app SmartEczema, which tracks symptoms and triggers, providing insights into the condition’s patterns and suggesting management strategies. These AI tools help patients manage their eczema more effectively by offering tailored advice and treatment options, reducing flare-ups, and improving overall skin health. By providing continuous monitoring and personalized recommendations, these platforms empower patients to take proactive steps in managing their condition, leading to better long-term outcomes.

 

9. Hair Loss Treatment

AI is making significant strides in hair loss treatment with technologies like those from HairMax. HairMax uses AI to analyze scalp images, identifying patterns of hair thinning and loss. Based on this analysis, the AI suggests personalized treatment plans, including laser therapy and topical solutions to promote hair regrowth. Another example is the AI-driven platform iRestore, which uses AI to customize low-level laser therapy (LLLT) treatments for hair loss. The AI adjusts treatment parameters based on individual responses, optimizing the effectiveness of the therapy. These AI-powered solutions help users address hair loss more effectively by providing targeted and personalized treatments, improving the chances of hair regrowth and restoring confidence. By continuously monitoring progress and adjusting treatments, these platforms ensure that users receive the most effective care for their specific hair loss condition.

 

10. Cosmetic Dermatology

AI is transforming cosmetic dermatology with platforms like MODA, which uses AI to simulate the results of cosmetic procedures. Patients can upload their photos and see potential outcomes of treatments like Botox, fillers, or laser therapy. The AI algorithms create realistic simulations, helping patients make informed decisions about undergoing cosmetic procedures. Another example is Crisalix, an AI-powered virtual reality tool that allows patients to visualize the results of cosmetic surgeries such as breast augmentation and rhinoplasty. These tools enhance the consultation process, enabling patients to set realistic expectations and dermatologists to provide better guidance. By visualizing potential outcomes, patients can choose treatments that align with their aesthetic goals, leading to higher satisfaction and better overall results. AI-driven cosmetic dermatology solutions improve patient experiences and streamline the decision-making process for both patients and practitioners.

 

Conclusion

AI is redefining the future of dermatology by introducing smarter, faster, and more patient-centric approaches to skin health management. As seen across various real-world applications, AI is not limited to skin cancer detection but extends to chronic disease monitoring, data-driven research, and advanced wound care. These innovations are helping clinicians make more informed decisions while improving access to dermatological care, especially in remote or resource-limited settings. With continued advancements in machine learning and medical imaging, AI is expected to further enhance diagnostic accuracy and personalize treatment strategies. However, successful adoption will depend on maintaining clinical validation, regulatory compliance, and patient trust. As highlighted by DigitalDefynd, the integration of AI into dermatology is not just a technological shift but a fundamental evolution in how skin conditions are diagnosed, monitored, and treated, paving the way for a more efficient and accessible healthcare system.