Examining labelling guidelines for AI‐based software as a medical device: A review and analysis of dermatology mobile applications in Australia

Author:

Oloruntoba Ayooluwatomiwa1ORCID,Ingvar Åsa1234ORCID,Sashindranath Maithili1,Anthony Ojochonu5,Abbott Lisa6ORCID,Guitera Pascale789,Caccetta Tony9,Janda Monika10,Soyer H. Peter10ORCID,Mar Victoria12

Affiliation:

1. School of Public Health and Preventive Medicine Monash University Melbourne Victoria Australia

2. Victorian Melanoma Service Alfred Health Melbourne Victoria Australia

3. Department of Dermatology, Skåne University Hospital Lund University Lund Sweden

4. Department of Clinical Sciences, Skåne University Hospital Lund University Lund Sweden

5. Faculty of Medicine, Nursing and Health Sciences Monash University Melbourne Victoria Australia

6. Melanoma Institute Australia The University of Sydney Sydney New South Wales Australia

7. Faculty of Medicine and Health The University of Sydney Sydney New South Wales Australia

8. Sydney Melanoma Diagnostic Centre Royal Prince Alfred Hospital Camperdown New South Wales Australia

9. Perth Dermatology Clinic Perth Western Australia Australia

10. Dermatology Research Centre, Frazer Institute The University of Queensland Brisbane Queensland Australia

Abstract

AbstractIn recent years, there has been a surge in the development of AI‐based Software as a Medical Device (SaMD), particularly in visual specialties such as dermatology. In Australia, the Therapeutic Goods Administration (TGA) regulates AI‐based SaMD to ensure its safe use. Proper labelling of these devices is crucial to ensure that healthcare professionals and the general public understand how to use them and interpret results accurately. However, guidelines for labelling AI‐based SaMD in dermatology are lacking, which may result in products failing to provide essential information about algorithm development and performance metrics. This review examines existing labelling guidelines for AI‐based SaMD across visual medical specialties, with a specific focus on dermatology. Common recommendations for labelling are identified and applied to currently available dermatology AI‐based SaMD mobile applications to determine usage of these labels. Of the 21 AI‐based SaMD mobile applications identified, none fully comply with common labelling recommendations. Results highlight the need for standardized labelling guidelines. Ensuring transparency and accessibility of information is essential for the safe integration of AI into health care and preventing potential risks associated with inaccurate clinical decisions.

Publisher

Wiley

Reference49 articles.

1. Artificial intelligence in healthcare: An essential guide for health leaders

2. AI in health and medicine

3. A new era: artificial intelligence and machine learning in prostate cancer

4. TGA.How the TGA regulates software‐based medical devices.2021p.1–30.

5. TGA.Is my software regulated?2021.

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