AI-Augmented Skin Cancer Diagnosis in Teledermatoscopy

Melanoma Clinical Trial

— AIDMel  

Official title:

AI-Augmented Skin Cancer Diagnosis in Teledermatoscopy: A Prospective Randomized Study

Clinical Trial Details — Status: Enrolling by invitation

Administrative data

• NCT number: NCT06080711
• Other study ID #: 960024
• Status: Enrolling by invitation
• Phase: N/A
• Start date: February 15, 2023
• Est. completion date: October 30, 2024

Study information

• Verified date: October 2023
• Source: Karolinska University Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

In this study an artificial intelligence (AI) tool for skin cancer diagnosis is implemented in a teleldermatoscopy platform. The aim is to study the effects on clinician diagnostic accuracy, management decisions, and confidence. Furthermore, this prospective randomized study investigates the role of human factors in determining clinician reliance on AI tools and the consequent accuracy in a real-world setting.

Description:

Deep-learning algorithms can potentially benefit many areas in healthcare, including the diagnosis of skin cancer using teledermatoscopy. However, there is a dearth of clinical, prospective research on human-AI interaction in diagnostic tasks that take human factors into account.

In this study we will examine the impact of such factors in a real-world setting where we integrate an algorithm in an existing teledermatoscopy platform that is used clinically at a tertiary hospital in Sweden. We will investigate what impact various implementations of AI tool output in relation to human factors have on diagnostic accuracy and management decisions.

Study subjects are recruited at the Department of Dermatology at Karolinska University Hospital and will be asked to rate prospective teledermatoscopic consults with and without AI-support. Each consult will be randomized into one of three workflows with or without one pre-defined implementation of the AI tool. Study subjects are also asked to complete two surveys with demographic information and questions relating to various human factors. Patients participating in the study will be diagnosed outside the study prior to inclusion without any involvement of an AI tool, notably by two experienced dermatologists who do not participate as study subjects.

Recruitment information / eligibility

• Status: Enrolling by invitation
• Enrollment: 30
• Est. completion date: October 30, 2024
• Est. primary completion date: June 30, 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Licensed physician

• Working at a dermatology clinic

• Sufficient knowledge in Swedish

• Written consent to participate

Exclusion Criteria:

• No experience of using dermatoscopy

• Does not wish to participate

• Incomplete answers

• Physicians that are involved in the patients' clinical care relating to the teledermoscopical consult

Related Conditions & MeSH terms

• Melanoma
• Skin Cancer
• Skin Neoplasms

Intervention

Other:
• AI assistance
Participants will be informed of the diagnostic probabilities for each of ten differential diagnoses according to the AI tool

Locations

Country	Name	City	State
Sweden	Karolinska University Hospital	Stockholm

Sponsors (4)

Lead Sponsor	Collaborator
Karolinska University Hospital	Karolinska Institutet, Medical University of Vienna, Stockholm School of Economics

Country where clinical trial is conducted

Sweden, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Diagnostic accuracy	Determine sensitivity, specificity, accuracy and AUROC in terms of diagnostic accuracy for dermatologists with vs without AI advice. Further, to investigate the role of the different workflows (diagnosis with or without AI with varying sequencing) and the influence of demographics and human factors (e.g. level of experience) on diagnostic accuracy	1 year
Primary	Accuracy of management decisions	Determine sensitivity, specificity, accuracy and AUROC in terms of accuracy for management decisions for dermatologists with vs without AI and investigate the role of the different workflows (with or without AI with varying sequencing) and the influence of demographics and human factors (e.g. level of experience) on management decisions (biopsy/surgery, no intervention, or follow-up)	1 year
Primary	Tendency to change initial diagnosis or management decision	Evaluate which factors affect the likelihood of a physician changing their evaluation after receiving algorithmic input	1 year
Primary	Self-reported confidence in diagnosis and management decisions	Investigate whether AI or other factors affect the physician's confidence in their diagnosis and management decisions	1 year