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Minimum labelling requirements for dermatology artificial intelligence-based Software as Medical Device (SaMD) : A consensus statement

Ingvar, Åsa LU orcid ; Oloruntoba, Ayooluwatomiwa ; Sashindranath, Maithili ; Miller, Robert ; Soyer, H. Peter ; Guitera, Pascale ; Caccetta, Tony ; Shumack, Stephen ; Abbott, Lisa and Arnold, Chris , et al. (2024) In Australasian Journal of Dermatology
Abstract

Background/Objectives: Artificial intelligence (AI) holds remarkable potential to improve care delivery in dermatology. End users (health professionals and general public) of AI-based Software as Medical Devices (SaMD) require relevant labelling information to ensure that these devices can be used appropriately. Currently, there are no clear minimum labelling requirements for dermatology AI-based SaMDs. Methods: Common labelling recommendations for AI-based SaMD identified in a recent literature review were evaluated by an Australian expert panel in digital health and dermatology via a modified Delphi consensus process. A nine-point Likert scale was used to indicate importance of 10 items, and voting was conducted to determine the... (More)

Background/Objectives: Artificial intelligence (AI) holds remarkable potential to improve care delivery in dermatology. End users (health professionals and general public) of AI-based Software as Medical Devices (SaMD) require relevant labelling information to ensure that these devices can be used appropriately. Currently, there are no clear minimum labelling requirements for dermatology AI-based SaMDs. Methods: Common labelling recommendations for AI-based SaMD identified in a recent literature review were evaluated by an Australian expert panel in digital health and dermatology via a modified Delphi consensus process. A nine-point Likert scale was used to indicate importance of 10 items, and voting was conducted to determine the specific characteristics to include for some items. Consensus was achieved when more than 75% of the experts agreed that inclusion of information was necessary. Results: There was robust consensus supporting inclusion of all proposed items as minimum labelling requirements; indication for use, intended user, training and test data sets, algorithm design, image processing techniques, clinical validation, performance metrics, limitations, updates and adverse events. Nearly all suggested characteristics of the labelling items received endorsement, except for some characteristics related to performance metrics. Moreover, there was consensus that uniform labelling criteria should apply across all AI categories and risk classes set out by the Therapeutic Goods Administration. Conclusions: This study provides critical evidence for setting labelling standards by the Therapeutic Goods Administration to safeguard patients, health professionals, consumers, industry, and regulatory bodies from AI-based dermatology SaMDs that do not currently provide adequate information about how they were developed and tested.

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organization
publishing date
type
Contribution to journal
publication status
epub
subject
keywords
artificial intelligence, Delphi consensus, dermatology, labelling, medical device
in
Australasian Journal of Dermatology
publisher
Wiley-Blackwell
external identifiers
  • scopus:85186581770
  • pmid:38419186
ISSN
0004-8380
DOI
10.1111/ajd.14222
language
English
LU publication?
yes
id
21444e5d-a966-40a8-90db-a3888f1e68f7
date added to LUP
2024-03-25 16:28:48
date last changed
2024-06-17 23:50:59
@article{21444e5d-a966-40a8-90db-a3888f1e68f7,
  abstract     = {{<p>Background/Objectives: Artificial intelligence (AI) holds remarkable potential to improve care delivery in dermatology. End users (health professionals and general public) of AI-based Software as Medical Devices (SaMD) require relevant labelling information to ensure that these devices can be used appropriately. Currently, there are no clear minimum labelling requirements for dermatology AI-based SaMDs. Methods: Common labelling recommendations for AI-based SaMD identified in a recent literature review were evaluated by an Australian expert panel in digital health and dermatology via a modified Delphi consensus process. A nine-point Likert scale was used to indicate importance of 10 items, and voting was conducted to determine the specific characteristics to include for some items. Consensus was achieved when more than 75% of the experts agreed that inclusion of information was necessary. Results: There was robust consensus supporting inclusion of all proposed items as minimum labelling requirements; indication for use, intended user, training and test data sets, algorithm design, image processing techniques, clinical validation, performance metrics, limitations, updates and adverse events. Nearly all suggested characteristics of the labelling items received endorsement, except for some characteristics related to performance metrics. Moreover, there was consensus that uniform labelling criteria should apply across all AI categories and risk classes set out by the Therapeutic Goods Administration. Conclusions: This study provides critical evidence for setting labelling standards by the Therapeutic Goods Administration to safeguard patients, health professionals, consumers, industry, and regulatory bodies from AI-based dermatology SaMDs that do not currently provide adequate information about how they were developed and tested.</p>}},
  author       = {{Ingvar, Åsa and Oloruntoba, Ayooluwatomiwa and Sashindranath, Maithili and Miller, Robert and Soyer, H. Peter and Guitera, Pascale and Caccetta, Tony and Shumack, Stephen and Abbott, Lisa and Arnold, Chris and Lawn, Craig and Button-Sloan, Alison and Janda, Monika and Mar, Victoria}},
  issn         = {{0004-8380}},
  keywords     = {{artificial intelligence; Delphi consensus; dermatology; labelling; medical device}},
  language     = {{eng}},
  publisher    = {{Wiley-Blackwell}},
  series       = {{Australasian Journal of Dermatology}},
  title        = {{Minimum labelling requirements for dermatology artificial intelligence-based Software as Medical Device (SaMD) : A consensus statement}},
  url          = {{http://dx.doi.org/10.1111/ajd.14222}},
  doi          = {{10.1111/ajd.14222}},
  year         = {{2024}},
}