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. 2024 May;65(3):e21-e29.
doi: 10.1111/ajd.14222. Epub 2024 Feb 28.

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

Åsa Ingvar  1   2   3   4 , Ayooluwatomiwa Oloruntoba  2 , Maithili Sashindranath  2 , Robert Miller  5 , H Peter Soyer  5   6 , Pascale Guitera  5   7   8   9 , Tony Caccetta  5   10 , Stephen Shumack  5   11 , Lisa Abbott  5   7   12 , Chris Arnold  13   14   15 , Craig Lawn  9   16 , Alison Button-Sloan  17 , Monika Janda  5   6   18 , Victoria Mar  1   2   5
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Minimum labelling requirements for dermatology artificial intelligence-based Software as Medical Device (SaMD): A consensus statement

Åsa Ingvar et al. Australas J Dermatol. 2024 May.

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 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.

Keywords: Delphi consensus; artificial intelligence; dermatology; labelling; medical device.

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References

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