TY - JOUR
T1 - Minimum labelling requirements for dermatology artificial intelligence-based Software as Medical Device (SaMD)
T2 - A consensus statement
AU - Ingvar, Åsa
AU - Oloruntoba, Ayooluwatomiwa
AU - Sashindranath, Maithili
AU - Miller, Robert
AU - Soyer, H. Peter
AU - Guitera, Pascale
AU - Caccetta, Tony
AU - Shumack, Stephen
AU - Abbott, Lisa
AU - Arnold, Chris
AU - Lawn, Craig
AU - Button-Sloan, Alison
AU - Janda, Monika
AU - Mar, Victoria
N1 - Publisher Copyright:
© 2024 The Authors. Australasian Journal of Dermatology published by John Wiley & Sons Australia, Ltd on behalf of Australasian College of Dermatologists.
PY - 2024/5
Y1 - 2024/5
N2 - 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.
AB - 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.
KW - Delphi consensus
KW - Artificial intelligence
KW - Dermatology
KW - Labelling
KW - Medical device
UR - http://www.scopus.com/inward/record.url?scp=85186581770&partnerID=8YFLogxK
U2 - 10.1111/ajd.14222
DO - 10.1111/ajd.14222
M3 - Article
C2 - 38419186
SN - 0004-8380
VL - 65
SP - e21-e29
JO - Australasian Journal of Dermatology
JF - Australasian Journal of Dermatology
IS - 3
ER -