Research & Evidence
ScanSkinAI Research, Clinical Evidence and Technical Publications
ScanSkinAI publishes clinical validation, technical methodology, product-transparency information and sourced public-health data to support responsible understanding of AI-assisted skin screening.
These resources explain how specific ScanSkinAI modules were developed and evaluated, what the reported results mean, and the limitations that must be considered.
ScanSkinAI is intended to support preliminary skin awareness and screening. It does not provide a medical diagnosis and does not replace a doctor or dermatologist.
Quick answer
ScanSkinAI research is published in four categories: clinical validation (the Accuracy Report, an independent physician case-by-case review), technical methodology (how the model was trained and evaluated), product transparency (what the Cancer Flag Module outputs and where human oversight is required) and sourced public-health data. Reports are citable, version-dated and archived on Zenodo with a permanent DOI. ScanSkinAI provides preliminary screening information only — it is not a medical diagnosis.
- Publisher
- Ivy AI Solutions Limited (ScanSkinAI)
- Evidence categories
- Clinical validation, methodology, product transparency, public-health data
- Permanent archive
- Zenodo DOI 10.5281/zenodo.21225287
- Last reviewed
- 2026-07-29
- Next review
- 2027-01-29
- Access
- Free to read and cite; not a medical diagnosis
Four categories of evidence
Each category answers a different question. They are not interchangeable, and results from one should not be read as results from another.
Clinical Validation
How ScanSkinAI outputs performed when reviewed case by case against clinical assessments by an independent physician reviewer.
ScanSkinAI Accuracy ReportTechnical Methodology
How the model was designed, trained, evaluated and documented — data preparation, architecture, evaluation approach and explainability.
AI Skin Cancer Screening MethodologyProduct Transparency
What one specific module does, what it outputs, where human oversight is required and where its intended-use boundaries end.
Cancer Flag ModulePublic-Health Data
Sourced incidence, mortality, survival and trend data from recognised national cancer registries and public-health authorities.
Skin Cancer Statistics
Clinical Validation
Clinical Validation Report
ScanSkinAI Accuracy Report
Review the independent clinical audit of ScanSkinAI screening outputs, including the study design, dataset, reviewer methodology, reported findings, triage performance and limitations.
- Evaluation type
- Independent dermatologist audit (Phase 1), retrospective review of production scans
- Number of reviewed cases
- 100 anonymised cases
- Conditions represented
- 80 skin conditions
- Skin-tone representation
- Fitzpatrick I–VI: I 7, II 14, III 21, IV 20, V 13, VI 5 cases (weighted toward III–IV)
- Reviewer
- Dr. Anand S. Urhekar, MD — independent physician reviewer
- Publication date
- 6 July 2026
- Last review date
- 6 July 2026
- Headline result (as reported)
- 95.3% clinically acceptable accuracy — a screening metric, not diagnostic accuracy
- Triage performance
- 8 over-triage and 2 under-triage cases out of 100 (2% under-triage rate)
- Main limitations
- Single-reviewer Phase 1 audit of 100 cases; photograph-based; not a diagnostic study; results apply only to the model version and dataset described in the report
Technical Methodology
Technical Whitepaper
AI Skin Cancer Screening Methodology
Explore the technical methodology used to develop and evaluate ScanSkinAI’s AI-assisted skin-screening technology, including data preparation, model architecture, evaluation approach, explainability and limitations.
- Resource type
- Company-authored technical whitepaper, archived on Zenodo with a permanent DOI
- Author / publisher
- Ivy AI Solutions Limited (developer of ScanSkinAI)
- Publication year
- 2026
- Model architecture described
- DINOv2-Large vision transformer (ViT-L/14) at 518×518 resolution
- Explainability
- Grad-CAM and SHAP attention inspection
- Quality framework
- ISO 13485 quality management and ISO 27001 information security
Citation
Ivy AI Solutions Limited. (2026). ScanSkinAI Cancer Flag Module: Training Methodology, Model Development, and Robustness Evaluation. Zenodo. https://doi.org/10.5281/zenodo.21225287
Resource type: Archived technical whitepaper (Zenodo, permanent DOI). This is a company-authored technical report archived with a permanent DOI. It has not undergone independent academic peer review.
Featured Report
Digital Skin Health Insights Report 2026
A descriptive analysis of 50,265 historical scan-level risk-band records and 45,270 medical skin-module AI-output records, with clear interpretation limits and methodology.
Product and Model Transparency
Product Transparency Report
Cancer Flag Module
Understand the intended use, model workflow, supported output categories, image-quality safeguards, explainability, responsible communication and limitations of the ScanSkinAI Cancer Flag Module.
- Intended use
- Non-diagnostic screening and triage support from an ordinary clinical photograph
- Module scope
- One module within ScanSkinAI — not the wider multi-condition screening system
- Output type
- Four categories: basal cell carcinoma, melanoma, squamous cell carcinoma, and benign or other lesions, communicated as a triage signal
- Image-quality requirements
- Image-quality safeguards gate low-quality photographs before a flag is returned
- Human oversight
- Clinician review is required; the module supports prioritisation, it does not decide care
- Non-diagnostic status
- Does not replace dermoscopy, biopsy, histopathology or in-person examination
- Important limitations
- Photograph-based; evaluated on the datasets and model version described in the whitepaper; not validated for every population, lesion type or imaging condition
Modules are evaluated separately
- Different modules may use different architectures.
- Different modules may cover different output categories.
- Different evaluations may use different datasets and metrics.
- Results from one module must not automatically be applied to another module.
Public-Health Data
Public-Health Data Reference
Skin Cancer Statistics
Explore sourced skin-cancer incidence, mortality, survival and trend data from recognised national cancer registries and public-health authorities.
- Countries covered
- United Kingdom, United States and Australia
- Types of statistics included
- New cases, deaths, five-year survival, lifetime risk and UV-attributable share
- Main data sources
- Cancer Research UK; American Cancer Society and SEER; AIHW and Cancer Council Australia
- Last reviewed
- July 2026
- Reference methodology
- Every figure on the page carries an inline citation to its originating registry or authority
Public-health statistics describe population-level patterns. They do not represent ScanSkinAI users, ScanSkinAI screening results or individual medical risk.
How to Interpret ScanSkinAI Evidence
Different evidence sources answer different questions.
- Clinical validation asks
- How did the system perform when reviewed against a defined set of clinical cases?
- Technical methodology asks
- How was a model designed, trained, evaluated and documented?
- Product transparency asks
- What does a specific module do, and what are its intended-use boundaries?
- Public-health data asks
- What do recognised registries report about disease incidence, mortality and survival?
Results from one evaluation should not be generalised beyond the model version, dataset, population, output categories and study design described in the relevant report.
Research Governance
- Research and content owner
- Ivy AI Solutions Limited (ScanSkinAI)
- Medical reviewer (this hub page)
- Content governance review pending
- Technical reviewer (this hub page)
- Content governance review pending
- Original publication date
- 29 July 2026
- Last reviewed
- 29 July 2026
- Next scheduled review
- 29 January 2027
- Version
- Hub page v1.0
- Correction contact
- info@scanskinai.com
This hub page summarises and links to the underlying reports. Reviewer information for each individual report is stated on that report. The Accuracy Report was reviewed independently by Dr. Anand S. Urhekar, MD; that review applies to the audit described in that report only and does not constitute review of this hub page. Citation guidance is provided in the Citing ScanSkinAI Research section below.
Research limitations: the evaluations linked here are limited in sample size, population coverage and model version. None of them establish diagnostic performance.
Transparency and Commercial Interest
- ScanSkinAI, developed by Ivy AI Solutions Limited, builds and commercialises the technology described in these resources.
- Some of these reports are company-authored, including the technical whitepaper and product-transparency documentation.
- Where an evaluation was carried out by an independent reviewer, that is stated explicitly in the relevant report.
- Company-authored methodology documentation is not the same as independent validation.
- Commercial relationships do not change the non-diagnostic intended use of the product.
- Read the full methodology and limitations in each report before interpreting any reported result.
Citing ScanSkinAI Research
ScanSkinAI Cancer Flag Module: Training Methodology, Model Development, and Robustness Evaluation
Ivy AI Solutions Limited · 2026 · Archived technical whitepaper (Zenodo, permanent DOI)
10.5281/zenodo.21225287Ivy AI Solutions Limited. (2026). ScanSkinAI Cancer Flag Module: Training Methodology, Model Development, and Robustness Evaluation. Zenodo. https://doi.org/10.5281/zenodo.21225287
ScanSkinAI Accuracy Report 2026
Ivy AI Solutions Limited · 2026 · Clinical validation report (independent dermatologist audit)
scanskinai.com/accuracy-reportScanSkinAI Accuracy Report 2026, Ivy AI Solutions Limited. https://www.scanskinai.com/accuracy-report
Skin Cancer Statistics 2026: UK, US & Australia
ScanSkinAI · 2026 · Public-health data reference
scanskinai.com/skin-cancer-statisticsScanSkinAI (2026). Skin Cancer Statistics 2026: UK, US & Australia. https://www.scanskinai.com/skin-cancer-statistics (Accessed [Month] [Year]).
Future Research
ScanSkinAI may publish additional responsibly aggregated digital skin-health insights after completing the necessary methodology, privacy, governance and clinical-review processes.
Related ScanSkinAI Information
Research & Evidence: Frequently Asked Questions
ScanSkinAI publishes a clinical validation report (the Accuracy Report), an AI skin cancer screening methodology whitepaper, a Cancer Flag Module product-transparency report, and registry-sourced skin cancer statistics. Each is version-dated and independently citable.
ScanSkinAI outputs have been reviewed case by case against clinical assessments by an independent physician reviewer, and the findings are published in the Accuracy Report. This is company-published validation, not peer-reviewed academic research, and results apply only to the model version, dataset and population described in the report.
Cite the archived Zenodo whitepaper as: Ivy AI Solutions Limited. (2026). ScanSkinAI Cancer Flag Module: Training Methodology, Model Development, and Robustness Evaluation. Zenodo. https://doi.org/10.5281/zenodo.21225287. Copy-ready citations for each report are listed in the citations section of this page.
Yes. The Cancer Flag Module technical whitepaper is permanently archived on Zenodo under DOI 10.5281/zenodo.21225287, and the Digital Skin Health Insights Report 2026 has its own Zenodo DOI.
No. ScanSkinAI provides preliminary screening and skin-awareness information only. It does not diagnose disease, cannot rule out disease, and does not replace assessment by a doctor or dermatologist. Any new, changing, bleeding or non-healing skin mark should be reviewed by a qualified healthcare professional.
Research is produced by Ivy AI Solutions Limited, the developer of ScanSkinAI, with clinical input from qualified medical reviewers. Publications state their version, review date and next scheduled review date.
Medical and Research Disclaimer
The information presented in the ScanSkinAI Research & Evidence library is provided for research transparency and general educational purposes.
ScanSkinAI is designed to support preliminary skin awareness and screening. It does not provide a medical diagnosis, rule out disease or replace assessment by a doctor or dermatologist.
Performance results apply only to the model version, dataset, evaluation method and population described in each report. Results from one module or evaluation must not be generalised to all ScanSkinAI products or all users.
Company-authored technical documentation is not equivalent to independent clinical validation or peer-reviewed academic research.