AI-Augmented Skin Cancer Diagnosis in Teledermatoscopy

NCT ID: NCT06080711

Last Updated: 2023-10-12

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Clinical Phase

NA

Total Enrollment

30 participants

Study Classification

INTERVENTIONAL

Study Start Date

2023-02-15

Study Completion Date

2024-10-30

Brief Summary

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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.

Detailed Description

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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.

Conditions

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Skin Cancer Melanoma

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

CROSSOVER

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

NONE

Study Groups

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Workflow 1

Standard of care

Group Type NO_INTERVENTION

No interventions assigned to this group

Workflow 2

Consult with AI assistance

Group Type EXPERIMENTAL

AI assistance

Intervention Type OTHER

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

Workflow 3

First workflow 1, then workflow 2

Group Type EXPERIMENTAL

AI assistance

Intervention Type OTHER

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

Interventions

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AI assistance

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

Intervention Type OTHER

Eligibility Criteria

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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
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Karolinska Institutet

OTHER

Sponsor Role collaborator

Medical University of Vienna

OTHER

Sponsor Role collaborator

Stockholm School of Economics

UNKNOWN

Sponsor Role collaborator

Karolinska University Hospital

OTHER

Sponsor Role lead

Responsible Party

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Jan Lapins

MD, PhD

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Karolinska University Hospital

Stockholm, , Sweden

Site Status

Countries

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Sweden

Other Identifiers

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960024

Identifier Type: -

Identifier Source: org_study_id

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