Skin CancerAIDMelNCT06080711
AI-Augmented Skin Cancer Diagnosis in Teledermatoscopy
A study testing AI assistance in people with Skin Cancer and Melanoma, run by Karolinska University Hospital at 1 site in Sweden.
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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Skin Cancer, Melanoma
- What is being tested?
- AI assistance
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 30 participants
- Where
- 1 siteSweden
- Study dates
- 15 Feb 2023to 30 Oct 2024
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Licensed physician
- Working at a dermatology clinic
- Sufficient knowledge in Swedish
- Written consent to participate
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
About this study
Official title: AI-Augmented Skin Cancer Diagnosis in Teledermatoscopy: A Prospective Randomized Study
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.
Read the detailed 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.
- Conditions
- Skin Cancer, Melanoma
- Being tested
- AI assistance
- Keywords
- melanoma, artificial intelligence, AI augmentation, deep learning, teledermatoscopy, dermatoscopy, skin cancer
- Medical subject headings
- Skin Neoplasms, Melanoma
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI assistanceOther
Participants will be informed of the diagnostic probabilities for each of ten differential diagnoses according to the AI tool
| Study group | Role | What they receive |
|---|---|---|
| Workflow 1 | No intervention | Standard of care |
| Workflow 2 | Experimental | Consult with AI assistance Receives: Other: AI assistance |
| Workflow 3 | Experimental | First workflow 1, then workflow 2 Receives: Other: AI assistance |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 1 year
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)
Measured: 1 year
Tendency to change initial diagnosis or management decision
Evaluate which factors affect the likelihood of a physician changing their evaluation after receiving algorithmic input
Measured: 1 year
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
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Oct 2023
- Study start15 Feb 2023
- Primary completion30 Jun 2024 (estimated)
- Study completion30 Oct 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Skin CancerStudies of the same condition, open and under way.
- More trials run by Karolinska University HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- None (open label)
- Enrolment
- 30 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Karolinska University Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Karolinska Institutet, Medical University of Vienna, Stockholm School of Economics
- Responsible party
- Principal investigator, Jan Lapins, Karolinska University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06080711
- Sponsor's protocol ID
- 960024
- Acronym
- AIDMel
- Status last verified
- Oct 2023
- First submitted
- 30 Aug 2023
- Last updated
- 12 Oct 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 12 Oct 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI-Augmented Skin Cancer Diagnosis in Teledermatoscopy: A Prospective Randomized Study
- ClinicalTrials.gov ID
- NCT06080711
- Acronym / sponsor ID
- AIDMel / 960024
- Conditions
- Skin Cancer, Melanoma
- Keywords
- melanoma, artificial intelligence, AI augmentation, deep learning, teledermatoscopy, dermatoscopy, skin cancer
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 30 participants (estimated)
- Lead sponsor
- Karolinska University HospitalUniversity, hospital or other
- Collaborators
- Karolinska Institutet, Medical University of Vienna, Stockholm School of Economics
- Study start
- 15 Feb 2023 (actual)
- Primary completion
- 30 Jun 2024 (estimated)
- Study completion
- 30 Oct 2024 (estimated)
- First posted
- 12 Oct 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
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.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- None (open label)
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Workflow 1 | No intervention | –Standard of care |
| Workflow 2 | Experimental | Other: AI assistanceConsult with AI assistance |
| Workflow 3 | Experimental | Other: AI assistanceFirst workflow 1, then workflow 2 |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- Licensed physician
- Working at a dermatology clinic
- Sufficient knowledge in Swedish
- Written consent to participate
Key exclusion criteria (4 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI assistanceParticipants will be informed of the diagnostic probabilities for each of ten differential diagnoses according to the AI tool | Other | Workflow 2, Workflow 3 |
Primary outcome
Diagnostic accuracy
Time frame: 1 year
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
Accuracy of management decisions
Time frame: 1 year
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)
Tendency to change initial diagnosis or management decision
Time frame: 1 year
Evaluate which factors affect the likelihood of a physician changing their evaluation after receiving algorithmic input
Self-reported confidence in diagnosis and management decisions
Time frame: 1 year
Investigate whether AI or other factors affect the physician's confidence in their diagnosis and management decisions
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Oct 2023.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI assistance in people with Skin Cancer and Melanoma, run by Karolinska University Hospital at 1 site in Sweden.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 12 Oct 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Sweden. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic accuracy (time frame: 1 year). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 12 Oct 2023.