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

Ingvar, Åsa (författare)
Lund University,Lunds universitet,Dermatologi och venereologi, Lund,Sektion III,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Hudcancerforskning vid Lunds universitet,Forskargrupper vid Lunds universitet,Dermatology and Venereology (Lund),Section III,Department of Clinical Sciences, Lund,Faculty of Medicine,LUCC: Lund University Cancer Centre,Other Strong Research Environments,LUSCaR- Lund University Skin Cancer Research group,Lund University Research Groups,Monash University,Skåne University Hospital,Alfred Health
Oloruntoba, Ayooluwatomiwa (författare)
Monash University
Sashindranath, Maithili (författare)
Monash University
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Miller, Robert (författare)
Soyer, H. Peter (författare)
University of Queensland
Guitera, Pascale (författare)
University of Sydney,Royal Prince Alfred Hospital
Caccetta, Tony (författare)
Shumack, Stephen (författare)
Royal North Shore Hospital
Abbott, Lisa (författare)
University of Sydney
Arnold, Chris (författare)
Lawn, Craig (författare)
University of Sydney
Button-Sloan, Alison (författare)
Janda, Monika (författare)
University of Queensland
Mar, Victoria (författare)
Monash University
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 (creator_code:org_t)
Engelska.
Ingår i: Australasian Journal of Dermatology. - 0004-8380.
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • 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.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Dermatologi och venereologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Dermatology and Venereal Diseases (hsv//eng)

Nyckelord

artificial intelligence
Delphi consensus
dermatology
labelling
medical device

Publikations- och innehållstyp

art (ämneskategori)
ref (ämneskategori)

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