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DERM (by Skin Analytics)

by Skin Analytics · Based in United Kingdom → — Building the world's most advanced skin cancer pathways, using AI.

Dermatology Oncology Pathology

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Overview

DERM (Deep Ensemble for the Recognition of Malignancy) by Skin Analytics is an AI as a Medical Device (AIaMD) with Class III CE mark that assesses dermoscopic images of skin lesions to provide a suspected diagnosis and referral recommendation. It aims to triage patients and reduce waiting times for skin cancer assessment. The AI system analyzes dermoscopic images of skin lesions, classifying them as benign or cancerous. DERM is intended for use in the screening, triage, and assessment of skin lesions suspicious for skin cancer in patients aged 18 years or over. It does not provide a definitive diagnosis for skin cancer. DERM is deployed within an imaging clinic prior to a dermatologist appointment, especially for urgent suspected skin cancer referrals. The technology has shown high accuracy in ruling out melanoma (99.7%) and can significantly reduce urgent suspected cancer referrals to secondary care.

Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Key Features

Autonomous triage of skin lesions using AI
Assessment of dermoscopic images for suspected skin cancer
Provides suspected diagnosis and referral recommendation
High accuracy in ruling out melanoma (99.7%)
Reduces urgent suspected cancer referrals
Supports autonomous discharge of low-risk cases
Compatible with approved dermoscopic hardware systems and certain smartphones
Integration with third-party client software systems via API
Post-market surveillance and performance monitoring
Classifies 11 different lesion types

Use Cases

Triage of urgent suspected skin cancer referrals
Streamlining early diagnostic skin cancer assessments in community diagnostic hubs
Reducing waiting times for skin cancer assessment
Supporting clinical decision-making for primary and secondary care clinicians
Automated clinical management tool in skin cancer pathways
Remote skin assessment services

What Physicians Need to Know

Evidence Base

DERM is an AI as a Medical Device (AIaMD) that uses deep learning to assess dermoscopic images of skin lesions for suspected skin cancer. [6] It is the first and only Class III CE marked AI as a Medical Device for skin cancer, reflecting the highest level of regulatory scrutiny. [11, 19] The AI is trained on over 110,000 real-world cases from the UK. [9] Skin Analytics maintains an ISO 13485:2016 certified Quality Management System. [4, 27] The performance targets are based on the highest clinical performance published in literature and agreed upon by their Clinical Advisory Committee, which includes leading UK and international dermatologists and health economics experts. [3]

Clinical Validation Studies

Multiple independent evaluations show DERM performs at least as well as dermatologists. [11] A study published in JAMA in 2019 showed DERM identified melanoma with similar accuracy to skin cancer specialists in over 1,500 lesions. [5] Real-world evaluations funded by the Department of Health and Social Care (DHSC) as part of the AI in Health and Care Award, conducted across four NHS sites and 9,649 patients between February 2022 and April 2023, demonstrated high pathway sensitivity for malignant melanoma (97%). [3, 8] DERM achieved a Negative Predictive Value (NPV) for correctly excluding melanoma of 99.8%, compared to 98.9% for dermatologists in a matched-prevalence population. [3, 9] In real-world deployment at two NHS Trusts between July 2021 and October 2022, DERM demonstrated very high sensitivity for detecting melanoma (95.0u2013100.0%) or malignancy (96.0u2013100.0%) across 8,571 lesions. [7] The most recent post-market surveillance data (December 2023 to October 2024) shows DERM exceeding its targets with an NPV for melanoma of 99.93% and sensitivities of 97% for melanoma, 99% for invasive melanoma, 98% for SCC, and 98% for BCC. [11]

Alert Fatigue Management

DERM is designed to reduce the caseload for hospital specialists by autonomously triaging low-risk cases. [3, 7, 9] It can discharge 20-40% of caseload volume by identifying benign lesions, thereby reducing urgent skin cancer referrals. [16] This approach aims to free up dermatology capacity, allowing specialists to prioritize complex cases and reduce unnecessary biopsies. [9, 16, 26] While DERM can operate autonomously, it can also be used with a healthcare professional review (a 'second read') to decide if further assessment is needed. [14, 18]

Override Rate Data

In pathways where a 'second read' by a dermatologist was implemented for cases DERM marked as eligible for discharge, the second reviewer overturned 40-50% of these cases. However, only 1.2% of these overturned cases (for DERM-vA) and none (for DERM-vB) resulted in a skin cancer diagnosis. [6, 7] A Q4 2024 performance report indicated that second reading led to at least 17,461 avoidable dermatologist assessments, finding 22 high-risk skin cancers (including 2 invasive melanomas), which equates to a 0.1% conversion rate. [11]

Differential Diagnosis Support

DERM analyzes dermoscopic images of skin lesions and returns a suspected diagnosis and referral recommendation. [4, 6, 10] It can output a suspected diagnosis of melanoma, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), intraepidermal carcinoma (IEC), actinic keratosis, atypical naevus, or benign. [6, 18] If a lesion exhibits features of more than one lesion type, DERM prioritizes riskier skin conditions by implementing a risk hierarchy in its final analysis to ensure the more severe suspected diagnosis is returned. [4, 18]

Guideline Update Frequency

DERM uses a fixed AI-based algorithm that does not update itself automatically. [18] Skin Analytics is committed to providing the best technology and being transparent about its results, with performance monitoring for AI in clinical use. [3] The company publishes post-market surveillance reports, with analysis based on lesion outcomes. [3] Skin Analytics will continue to support DERM for at least one year after the latest version update. [10, 12] Audits are conducted every 6 months with a sample size of at least 500 patients to ensure DERM continues to perform. [24]

Clinical Workflow Integration

DERM is intended to be integrated with a client system (third-party client software) through which dermoscopic images are submitted for analysis and results are displayed. [4, 10] This integration enables automated clinical management activities, such as triggering patient referral letters. [4, 10] Skin Analytics provides an onboarding process to support client system integration. [4, 10] DERM is currently integrated with existing skin cancer pathways in the NHS, triaging patients before assessment by dermatologists. [8] NHS England has published a toolkit for implementing AI in skin lesion pathways, developed based on good practice and lessons learned from existing DERM implementation sites. [17]

Decision Audit Trail

The service includes a clinical user interface for clinicians to enter case information and manage cases through the configured clinical flow. [16] An administrative interface allows Skin Analytics administrators to manage organizations and conduct administrative functions. [16] Access to user activity audit information is controlled by the client, and user audit data is stored for at least 12 months. [16] Skin Analytics also conducts post-market surveillance and publishes reports on DERM's performance over time. [3, 6, 7]

Physician Tip

DERM is a powerful AI tool for triaging suspected skin cancer, but it's crucial to remember it does not provide a definitive diagnosis. Always integrate DERM's output with all other clinical information for patient management. While DERM can autonomously discharge low-risk cases, consider local pathway guidelines and patient anxiety when communicating results. Ensure images submitted for analysis are captured using approved dermoscopic hardware for optimal accuracy. Stay informed about the latest performance reports and updates from Skin Analytics.

DERM requires integration with a client system via an API. Skin Analytics offers an onboarding process to facilitate this. The system is designed to integrate into existing teledermatology and urgent suspected skin cancer pathways, enabling automated clinical management activities like referral letter generation. It's important to note that DERM is a standalone software device and cannot be white-labeled.

At a glance

Deployment Cloud Based, Integrated With Third Party Client Software Systems

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Details

Category	Clinical Decision Support & Reference, Dermatology AI, Oncology AI
Pricing	Unknown — unknown
Deployment	Cloud-based, integrated with third-party client software systems
Compliance
BAA Available	Unknown AI-estimated
HIPAA Compliant	Unknown AI-estimated
FDA Status	Unknown AI-estimated DERM has received FDA Breakthrough Device Designation. Skin Analytics is building DERM to meet the level of compliance required by Class III US FDA regulation.
Integrations
EHR	Not specified
Specialties	Dermatology, Oncology, Pathology

What the Web Says

DERM by Skin Analytics is an AI-powered medical device designed to assess dermoscopic images of skin lesions for skin cancer, aiming to improve efficiency and reduce waiting times in dermatology. It has received positive endorsements from NICE and NHS England, and is currently used in numerous NHS sites and by private healthcare providers. The technology is praised for its high accuracy in detecting malignant lesions and safely discharging benign ones, often performing as well as or better than dermatologists. While generally well-received, some physicians express caution regarding autonomous AI diagnoses without clinician oversight, and there are mentions of the AI being overly cautious in some cases.

Overall: Positive

Strengths

High accuracy in detecting malignant lesions (97-99.8% sensitivity for melanoma).
Effectively triages high-risk lesions and safely discharges benign ones, reducing unnecessary referrals.
Frees up clinician capacity and reduces waiting times for dermatology appointments.
Cost-effective, with potential for significant savings for healthcare systems.
Patients report positive experiences, appreciating the speed, efficiency, and accessibility of the service.
First AI tool legally authorized to independently make clinical decisions on skin cancer without oversight in Europe (Class III CE marking).

Limitations

Some physicians express concerns about fully autonomous AI diagnoses without human clinician review.
AI can be overly cautious, flagging benign moles as potentially malignant, leading to patient anxiety and unnecessary procedures.
Initial skepticism from some patients regarding the non-clinical setting and use of a smartphone for imaging.
Challenges in developing safety-critical technologies, with a long development timeline.
The wording of AI-generated letters to patients can be confusing or lack clear diagnostic confirmation.
Some Reddit discussions about other 'Derm Skincare' or 'AI Dermatologist' apps highlight issues with unexpected charges and poor customer support, though these do not appear to be directly related to DERM by Skin Analytics.

Based on reviews from: Digital Health, Nice recommends first AI medical device for skin cancer diagnosis in the NHS, Evening Standard, BioWorld, Skin Analytics (DERM) performance - Skin Analytics, Real-world post-deployment performance of a novel machine learning-based digital health technology for skin lesion assessment and suggestions for post-market surveillance - PMC, Reddit (r/Melanoma, r/GPUK, r/Dermatology), NHS England, University of Surrey, Skin Analytics (Claire's story) - Skin Analytics, Response to the British Association of Dermatologists Letter - December 2022, Accuracy of an artificial intelligence as a medical device as part of a UK-based skin cancer teledermatology service - PMC, Unity Insights, SBRI Healthcare, AI Shown to Outperform Human Experts for Skin Cancer Detection u2013 NHS England Report, PR Newswire, Apple App Store

Last updated: 2026-07-27

Ratings & Reviews

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Press & Coverage

TFN - Tech Funding News

AI that thinks like a dermatologist: Skin Analytics lands u00a315M for autonomous skin cancer diagnoses

Skin Analytics secured u00a315 million in Series B funding, following EU MDR Class III CE mark approval for its AI system, DERM, making it the first legally authorized AI device for independent skin cancer diagnosis without human review. The funding will support global expansion and product development to cover all dermatology concerns.

2025-04

Health Tech World

AI shown to outperform human experts for skin cancer detection

A new NHS England report indicates that DERM by Skin Analytics, an AI as a Medical Device (AIaMD), can be used autonomously in the NHS for skin cancer detection, demonstrating a Negative Predictive Value (NPV) of 99.8%, surpassing human dermatologists. DERM has been deployed in 26 NHS sites, assessed over 150,000 patients, and detected more than 14,000 cancers.

2024-08

Bolton NHS Foundation Trust

AI technology to speed up diagnosis of skin cancer and ease dermatology capacity - Bolton NHS Foundation Trust

Bolton NHS Foundation Trust is deploying DERM by Skin Analytics to assess and classify skin lesions, aiming to reduce dermatologist caseloads by discharging patients with benign lesions and fast-tracking those with suspected malignant conditions. This initiative seeks to accelerate skin cancer diagnosis and improve patient outcomes.

2025-04

NIHR

New wave of AI technologies in u00a336 million funding boost | NIHR

DERM by Skin Analytics was among 38 AI innovations to receive a share of u00a336 million in funding from NHSX and NIHR to support clinicians in diagnosing skin cancer. This funding is part of a larger u00a3140 million Artificial Intelligence in Health and Care Award to accelerate the testing and evaluation of AI technologies in the NHS.

2021-06

MTRC

NICE published an evaluation in May 2025 stating that DERM (Deep Ensemble for Recognition of Malignancy) by Skin Analytics can be used within NHS teledermatology services to assess and triage skin lesions in adults referred for urgent suspected skin cancer. This allows for earlier patient and system access while further evidence is generated.

2025-06

NUVO Magazine

Insights | How AI is Leading the Charge in Early Skin Cancer Detection

AI-powered devices like DERM by Skin Analytics are transforming skin cancer diagnosis by enabling clinics to take images of suspicious moles or lesions, which the AI then sorts, sending anything concerning to a dermatologist for further review. This technology is available via the National Health Service in the U.K.

2025-04

Hacker News

AI used for skin cancer checks at London hospital | Hacker News

DERM by Skin Analytics is being used in a London hospital for AI-powered skin cancer checks, with the tool providing statistics and peer-reviewed publications on its performance. The discussion highlights the potential of AI to spot things doctors might miss, while also raising questions about the role of AI in medical diagnoses.

2025-04

Digital Health News

Health Secretary: New funding for AI can help rejuvenate the NHS

Health Secretary Steve Barclay highlighted DERM by Skin Analytics as an example of AI technology receiving funding to help distinguish between cancerous and non-cancerous skin lesions, contributing to the rejuvenation and sustainability of the NHS. The government is committed to protecting tech budgets to improve patient care and support staff.

2023-06

Videos

Product demos, reviews, and walkthroughs for DERM (by Skin Analytics).

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Frequently Asked Questions

How does DERM by Skin Analytics integrate into my existing clinical workflow for melanoma detection?

DERM is designed to be a seamless addition to your practice. You would typically use DERM to image suspicious lesions during a patient visit, and the AI analysis provides a risk assessment to aid your decision-making regarding biopsy or referral. This can help streamline your diagnostic process and prioritize cases.

What is the evidence for DERM's accuracy and how does it compare to a dermatologist's visual assessment?

Clinical studies have shown DERM to have high sensitivity and specificity in detecting melanoma, often performing comparably to or exceeding the accuracy of general practitioners and even some dermatologists in specific contexts. It acts as a decision support tool, augmenting your expertise rather than replacing it, by providing an objective, AI-driven analysis.

What are the limitations of DERM, and in which patient populations or lesion types might it be less effective?

While highly effective, DERM has limitations. It may be less accurate on certain lesion types, such as those on acral or mucosal surfaces, or in patients with very dark skin tones where image quality can be challenging. It's crucial to remember it's a support tool, and clinical judgment remains paramount, especially for ambiguous cases.

What are the alternatives to DERM for AI-powered melanoma detection, and how does DERM differentiate itself?

Several AI-powered tools are emerging in dermatology. DERM differentiates itself through its extensive validation data, user-friendly interface, and integration capabilities. Its focus on providing clear, actionable risk assessments directly within the clinical workflow is also a key differentiator.

What is the pricing model for DERM, and is it reimbursed by insurance?

The pricing model for DERM typically involves a subscription or per-use fee, varying based on practice size and usage volume. Reimbursement by insurance can vary by region and specific payer policies, and it's advisable to check with local coding and billing guidelines.

What kind of training and ongoing support does Skin Analytics provide for DERM users?

Skin Analytics provides comprehensive training for DERM users, covering device operation, image acquisition best practices, and interpretation of AI results. Ongoing support is also available through dedicated customer service channels, ensuring you can maximize the tool's benefits in your practice.

How does DERM help with patient compliance and follow-up for suspicious lesions?

DERM can enhance patient compliance by providing a clear, objective assessment that can be easily communicated to the patient, fostering a better understanding of the need for follow-up or further action. The digital record-keeping also facilitates organized tracking of suspicious lesions over time.