Evidence map›Paper›PMID 37261324›Full record

ArticleEClinicalMedicine2023

Artificial intelligence in mobile health for skin cancer diagnostics at home (AIM HIGH): a pilot feasibility study.

Anna M Smak Gregoor, Tobias E Sangers, Just Ah Eekhof, Sydney Howe, Jeroen Revelman, Romy Jm Litjens, Mohammed Sarac, Patrick Je Bindels, Tobias Bonten, Rik Wehrens and 1 more

Open access · goldAbstract read
In one paragraph

Article in EClinicalMedicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 2 pooled it
5.5field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 24 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. AI in Aesthetic/Cosmetic Dermatology: Current and Future.Journal of cosmetic dermatology · 2025
    Review
  7. Review
  8. Review
  9. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 4 institutions in 1 country.

Anna M Smak GregoorDepartment of Dermatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, the Netherlands.
Tobias E SangersDepartment of Dermatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, the Netherlands.
Just Ah EekhofDepartment of Public Health and Primary Care, Leiden University Medical Centre, Leiden, the Netherlands.
Sydney HoweSchool of Health Policy and Management, Erasmus University, Rotterdam, the Netherlands.
Jeroen RevelmanDepartment of Dermatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, the Netherlands.
Romy Jm LitjensDepartment of Dermatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, the Netherlands.
Mohammed SaracDepartment of Dermatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, the Netherlands.
Patrick Je BindelsGeneral Practice, Erasmus MC Rotterdam, Rotterdam, the Netherlands.
Tobias BontenDepartment of Public Health and Primary Care, Leiden University Medical Centre, Leiden, the Netherlands.
Rik WehrensSchool of Health Policy and Management, Erasmus University, Rotterdam, the Netherlands.
Marlies WakkeeDepartment of Dermatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, the Netherlands.
Erasmus MC Cancer Institute · NLErasmus University Rotterdam · NLLeiden University · NLErasmus MC · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Artificial intelligence (AI)-based mobile phone apps (mHealth) have the potential to streamline care for suspicious skin lesions in primary care. This study aims to investigate the conditions and feasibility of a study that incorporates an AI-based app in primary care and evaluates its potential impact. Methods: We conducted a pilot feasibility study from November 22nd, 2021 to June 9th, 2022 with a mixed-methods design on implementation of an AI-based mHealth app for skin cancer detection in three primary care practices in the Netherlands (Rotterdam, Leiden and Katwijk). The primary outcome was the inclusion and successful participation rate of patients and general practitioners (GPs). Secondary outcomes were the reasons, facilitators and barriers for successful participation and the potential impact in both pathways for future sample size calculations. Patients were offered use of an AI-based mHealth app before consulting their GP. GPs assessed the patients blinded and then unblinded to the app. Qualitative data included observations and audio-diaries from patients and GPs and focus-groups and interviews with GPs and GP assistants. Findings: Fifty patients were included with a median age of 52 years (IQR 33.5-60.3), 64% were female, and 90% had a light skin type. The average patient inclusion rate was 4-6 per GP practice per month and 84% (n = 42) successfully participated. Similarly, in 90% (n = 45 patients) the GPs also successfully completed the study. GPs never changed their working diagnosis, but did change their treatment plan (n = 5) based on the app's assessments. Notably, 54% of patients with a benign skin lesion and low risk rating, indicated that they would be reassured and cancel their GP visit with these results (p < 0.001). Interpretation: Our findings suggest that studying implementation of an AI-based mHealth app for detection of skin cancer in the hands of patients or as a diagnostic tool used by GPs in primary care appears feasible. Preliminary results indicate potential to further investigate both intended use settings. Funding: SkinVision B.V.

Indexed as

Artificial intelligenceConvolutional neural networkGeneral practitionersMobile healthPrimary careSkin cancer

Identifiers

PMID37261324
PMCPMC10227364
OpenAlexW4378193666

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.