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; Marlies Wakkee

doi:10.1016/j.eclinm.2023.102019

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

EClinicalMedicine. 2023 May 25:60:102019. doi: 10.1016/j.eclinm.2023.102019. eCollection 2023 Jun.

Affiliations

¹ Department of Dermatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, the Netherlands.
² Department of Public Health and Primary Care, Leiden University Medical Centre, Leiden, the Netherlands.
³ School of Health Policy and Management, Erasmus University, Rotterdam, the Netherlands.
⁴ General Practice, Erasmus MC Rotterdam, Rotterdam, the Netherlands.

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.

Keywords: Artificial intelligence; Convolutional neural network; General practitioners; Mobile health; Primary care; Skin cancer.