Patient perspectives of artificial intelligence as a medical device in a skin cancer pathway.

AI as a medical device Skin Analytics artificial intelligence deep ensemble for the recognition of malignancy (DERM) dermatology medical device patient perspectives skin cancer

Journal

Frontiers in medicine
ISSN: 2296-858X
Titre abrégé: Front Med (Lausanne)
Pays: Switzerland
ID NLM: 101648047

Informations de publication

Date de publication:
2023
Historique:
received: 16 07 2023
accepted: 27 10 2023
medline: 4 12 2023
pubmed: 4 12 2023
entrez: 4 12 2023
Statut: epublish

Résumé

The use of artificial intelligence as a medical device (AIaMD) in healthcare systems is increasing rapidly. In dermatology, this has been accelerated in response to increasing skin cancer referral rates, workforce shortages and backlog generated by the COVID-19 pandemic. Evidence regarding patient perspectives of AIaMD is currently lacking in the literature. Patient acceptability is fundamental if this novel technology is to be effectively integrated into care pathways and patients must be confident that it is implemented safely, legally, and ethically. A prospective, single-center, single-arm, masked, non-inferiority, adaptive, group sequential design trial, recruited patients referred to a teledermatology cancer pathway. AIaMD assessment of dermoscopic images were compared with clinical or histological diagnosis, to assess performance (NCT04123678). Participants completed an online questionnaire to evaluate their views regarding use of AIaMD in the skin cancer pathway. Two hundred and sixty eight responses were received between February 2020 and August 2021. The majority of respondents were female (57.5%), ranged in age between 18 and 93 years old, Fitzpatrick type I-II skin (81.3%) and all 6 skin types were represented. Overall, there was a positive sentiment regarding potential use of AIaMD in skin cancer pathways. The majority of respondents felt confident in computers being used to help doctors diagnose and formulate management plans (median = 70; interquartile range (IQR) = 50-95) and as a support tool for general practitioners when assessing skin lesions (median = 85; IQR = 65-100). Respondents were comfortable having their photographs taken with a mobile phone device (median = 95; IQR = 70-100), which is similar to other studies assessing patient acceptability of teledermatology services. To the best of our knowledge, this is the first comprehensive study evaluating patient perspectives of AIaMD in skin cancer pathways in the UK. Patient involvement is essential for the development and implementation of new technologies. Continued end-user feedback will allow refinement of services to ensure patient acceptability. This study demonstrates patient acceptability of the use of AIaMD in both primary and secondary care settings.

Identifiants

pubmed: 38046409
doi: 10.3389/fmed.2023.1259595
pmc: PMC10693417
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1259595

Informations de copyright

Copyright © 2023 Kawsar, Hussain, Kalsi, Kemos, Marsden and Thomas.

Déclaration de conflit d'intérêts

LT is a clinical advisor to Skin Analytics Ltd., has received Skin Analytics shares or share options; has received research funding support from Skin Analytics (salaries and equipment) and AIaMD deployment programme; has received reimbursement of conference fees, travel and accommodation costs from Skin Analytics to present research results; LT has received financial remuneration for separate programme of work as a consultant by Skin Analytics; has received grant funding from NHSX and CW+; has received paid honoraria to lecture for Almirall; was supported to attend a conference by Abbvie and Janssen; and holds multiple unpaid leadership roles. HM is an employee of Skin Analytics Ltd., and has received Skin Analytics shares or share options. DK is an employee of Skin Analytics Ltd., and has received Skin Analytics shares or share options. PK was previously a contractor with Skin Analytics Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Skin Analytics, London, UK sponsored and funded this study, as part of an Innovate UK BioMedical Catalyst project, and was involved with the study design, data collection, statistical analysis and interpretation of the data.

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Auteurs

Anusuya Kawsar (A)

Chelsea and Westminster Hospital NHS Foundation Trust, London, United Kingdom.

Khawar Hussain (K)

Chelsea and Westminster Hospital NHS Foundation Trust, London, United Kingdom.

Dilraj Kalsi (D)

Skin Analytics Ltd., London, United Kingdom.

Polychronis Kemos (P)

Skin Analytics Ltd., London, United Kingdom.
Blizard Institute, Queen Mary University of London, London, United Kingdom.

Helen Marsden (H)

Skin Analytics Ltd., London, United Kingdom.

Lucy Thomas (L)

Chelsea and Westminster Hospital NHS Foundation Trust, London, United Kingdom.

Classifications MeSH