Clinical trial · Interventional
AI Augmented Training for Skin Specialists
Artificial Intelligence Augmented Training in Skin Cancer Diagnostics for Skin Cancer Specialists
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
Background: The worldwide incidence of skin cancer has been rising for 50 years, in particular the incidence of malignant melanoma has increased approx. 2-7% annually and is the most common cancer amongst Danes aged 15-34. Currently there is a significant amount of misdiagnosis of skin cancer and mole cancer, and most excised skin lesions are benign. Previous studies have shown that there is no significant increase in doctors diagnostic accuracy during the first 6 years of clinical work. The resources spend on healthy people could be put to better use, if the Benign-Malignant Ratio could be lowered. This could potentially be done by better educating the doctors during their everyday clinical practice. Aim: The aim of this study is to investigate the dose/response effect of an AI augmented training and clinical feedback on the diagnostic accuracy of skin cancer and clinical decisions among doctors from specialized skin cancer centers. Research question: How much specialized doctors need to train before their diagnostic accuracy and clinical decisions change?
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Melanoma | Melanoma | ONTOLOGY_EXACT | 0.98 |
| Skin Cancer | Malignant Skin Neoplasm | ALIAS | 0.90 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| DermLoop Learn | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- type
- OTHER
- label
- Group A
- description
- This group will receive access to the AI augmented digital online educational system and its two modules (Training Module and Clinical Feedback Module). They will receive continuous clinical feedback on their registered lesions.
- interventionNames
- Other: DermLoop Learn
- type
- NO_INTERVENTION
- label
- Group B
- description
- This group is withheld their access to the AI augmented digital online educational system for 2 months. After the 2 months delay, the subjects in the group are given the same access as the participants in Group A.
Primary outcomes (1)
- measure
- Dose/Response
- timeFrame
- 2 years
- description
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
Inclusion Criteria: * Doctors are required to work at a specialized skin department (dermatology or plastic surgery or the like). * Doctors must be registered authorized health personnel Exclusion Criteria: * Doctors that have previously received access to the DermLoop Learn educational intervention * Doctors with less than 2 months left of their affiliation with their current department of employment
References
Publications (0)
Data not yet available