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Examining labelling guidelines for AI-based software as a medical device : A review and analysis of dermatology mobile applications in Australia

Oloruntoba, Ayooluwatomiwa ; Ingvar, Åsa LU orcid ; Sashindranath, Maithili ; Anthony, Ojochonu ; Abbott, Lisa ; Guitera, Pascale ; Caccetta, Tony ; Janda, Monika ; Soyer, H. Peter and Mar, Victoria (2024) In Australasian Journal of Dermatology
Abstract

In recent years, there has been a surge in the development of AI-based Software as a Medical Device (SaMD), particularly in visual specialties such as dermatology. In Australia, the Therapeutic Goods Administration (TGA) regulates AI-based SaMD to ensure its safe use. Proper labelling of these devices is crucial to ensure that healthcare professionals and the general public understand how to use them and interpret results accurately. However, guidelines for labelling AI-based SaMD in dermatology are lacking, which may result in products failing to provide essential information about algorithm development and performance metrics. This review examines existing labelling guidelines for AI-based SaMD across visual medical specialties, with... (More)

In recent years, there has been a surge in the development of AI-based Software as a Medical Device (SaMD), particularly in visual specialties such as dermatology. In Australia, the Therapeutic Goods Administration (TGA) regulates AI-based SaMD to ensure its safe use. Proper labelling of these devices is crucial to ensure that healthcare professionals and the general public understand how to use them and interpret results accurately. However, guidelines for labelling AI-based SaMD in dermatology are lacking, which may result in products failing to provide essential information about algorithm development and performance metrics. This review examines existing labelling guidelines for AI-based SaMD across visual medical specialties, with a specific focus on dermatology. Common recommendations for labelling are identified and applied to currently available dermatology AI-based SaMD mobile applications to determine usage of these labels. Of the 21 AI-based SaMD mobile applications identified, none fully comply with common labelling recommendations. Results highlight the need for standardized labelling guidelines. Ensuring transparency and accessibility of information is essential for the safe integration of AI into health care and preventing potential risks associated with inaccurate clinical decisions.

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organization
publishing date
type
Contribution to journal
publication status
in press
subject
keywords
artificial intellgience, melanoma, mobile applications, skin cancer, Software as a Medical Device
in
Australasian Journal of Dermatology
publisher
Wiley-Blackwell
external identifiers
  • pmid:38693690
  • scopus:85192180050
ISSN
0004-8380
DOI
10.1111/ajd.14269
language
English
LU publication?
yes
id
db6811c8-0854-41c1-a1be-7752ba8b410f
date added to LUP
2024-05-16 13:02:03
date last changed
2024-05-30 14:53:55
@article{db6811c8-0854-41c1-a1be-7752ba8b410f,
  abstract     = {{<p>In recent years, there has been a surge in the development of AI-based Software as a Medical Device (SaMD), particularly in visual specialties such as dermatology. In Australia, the Therapeutic Goods Administration (TGA) regulates AI-based SaMD to ensure its safe use. Proper labelling of these devices is crucial to ensure that healthcare professionals and the general public understand how to use them and interpret results accurately. However, guidelines for labelling AI-based SaMD in dermatology are lacking, which may result in products failing to provide essential information about algorithm development and performance metrics. This review examines existing labelling guidelines for AI-based SaMD across visual medical specialties, with a specific focus on dermatology. Common recommendations for labelling are identified and applied to currently available dermatology AI-based SaMD mobile applications to determine usage of these labels. Of the 21 AI-based SaMD mobile applications identified, none fully comply with common labelling recommendations. Results highlight the need for standardized labelling guidelines. Ensuring transparency and accessibility of information is essential for the safe integration of AI into health care and preventing potential risks associated with inaccurate clinical decisions.</p>}},
  author       = {{Oloruntoba, Ayooluwatomiwa and Ingvar, Åsa and Sashindranath, Maithili and Anthony, Ojochonu and Abbott, Lisa and Guitera, Pascale and Caccetta, Tony and Janda, Monika and Soyer, H. Peter and Mar, Victoria}},
  issn         = {{0004-8380}},
  keywords     = {{artificial intellgience; melanoma; mobile applications; skin cancer; Software as a Medical Device}},
  language     = {{eng}},
  publisher    = {{Wiley-Blackwell}},
  series       = {{Australasian Journal of Dermatology}},
  title        = {{Examining labelling guidelines for AI-based software as a medical device : A review and analysis of dermatology mobile applications in Australia}},
  url          = {{http://dx.doi.org/10.1111/ajd.14269}},
  doi          = {{10.1111/ajd.14269}},
  year         = {{2024}},
}