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

• Published in: Australasian journal of dermatology Vol. 65; no. 3; pp. e21 - e29
• Main Authors: Ingvar, Åsa, Oloruntoba, Ayooluwatomiwa, Sashindranath, Maithili, Miller, Robert, Soyer, H. Peter, Guitera, Pascale, Caccetta, Tony, Shumack, Stephen, Abbott, Lisa, Arnold, Chris, Lawn, Craig, Button‐Sloan, Alison, Janda, Monika, Mar, Victoria
• Format: Journal Article
• Language: English
• Published: Australia Wiley Subscription Services, Inc 01.05.2024
• Subjects: Artificial Intelligence; Australia; Business metrics; Clinical Medicine; Consensus; Delphi consensus; Delphi Technique; Dermatologi och venereologi; Dermatology; Dermatology - standards; Dermatology and Venereal Diseases; Humans; Image processing; Klinisk medicin; Labeling; labelling; Literature reviews; Medical and Health Sciences; medical device; Medical equipment; Medical personnel; Medicin och hälsovetenskap; Product Labeling - standards; Software; Australia; dermatology; labelling; Delphi consensus; medical device; artificial intelligence
• ISSN: 0004-8380; 1440-0960; 1440-0960
• DOI: 10.1111/ajd.14222

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.