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

In 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.

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

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.

Informing a position statement on the use of artificial intelligence in dermatology in Australia.

Artificial Intelligence (AI) is the ability for computers to simulate human intelligence. In dermatology, there is substantial interest in using AI to identify skin lesions from images. Due to increasing research and interest in the use of AI, the Australasian College of Dermatologists has developed a position statement to inform its members of appropriate use of AI. This article presents the ACD Position Statement on the use of AI in dermatology, and provides explanatory information that was used to inform the development of this statement.

Lupus vulgaris: difficulties in diagnosis.

Lupus vulgaris is one of the most common forms of cutaneous tuberculosis. It presents a diagnostic challenge due to its paucibacillary nature. This is a report of a case of a delayed diagnosis of lupus vulgaris, presenting as perianal and peristomal plaques, followed by a review of the diagnostic tools for lupus vulgaris and their limitations.

Multiple minute digitate hyperkeratosis: a proposed algorithm for the digitate keratoses.

BACKGROUND: Multiple minute digitate hyperkeratosis (MMDH) is a rare disorder of keratinization with many different names. OBJECTIVE: We present a case of MMDH and review the literature. We propose and discuss the classification the digitate keratoses, which include MMDH, lichen spinulosus, phrynoderma, spiny keratoderma, arsenical keratosis, multiple filiform verrucae, postirradiation digitate keratosis, trichodysplasia spinulosa, and hyperkeratotic spicules. We present a table of suggested and synonymous terms and propose a diagnostic algorithm for these digitate keratoses. METHODS: A literature search using PubMed and MEDLINE was performed. This included the search terms "MMDH," "familial disseminated filiform hyperkeratosis," "punctate porokeratotic keratoderma," "disseminated spiked keratosis," "minute aggregate keratosis," "digitate keratosis," "conical keratosis," "hyperkeratotic spicules," and "music box spine dermatosis." A case of MMDH in an 89-year-old woman is described. RESULTS: The digitate keratoses are presented alongside their synonymous terms and are divided into those that are generalized or localized using an algorithm. LIMITATIONS: Separate disease entities are likely to arise within the digitate keratoses with increased reporting of immunohistochemical keratin analysis and molecular genetic studies. CONCLUSION: We report a new case of MMDH and provide a clinical approach to diagnosis of the digitate keratoses.

Nephrogenic systemic fibrosis associated with liver transplantation, renal failure and gadolinium.

A 47-year-old man underwent liver transplantation for cirrhosis secondary to hepatitis C and alcoholism. This was complicated by primary donor liver dysfunction and acute renal failure requiring dialysis. Gadolinium magnetic resonance cholangiopancreatography was performed 2 weeks post transplant, and a second successful liver transplant was performed 1 week later. Shortly after this, the patient developed rapidly progressive erythematous plaques over his abdomen, lower and upper limbs. There was marked oedema and skin induration. Fibrosis severely limited his mobility, leaving him wheelchair-bound. An abdominal plaque biopsy revealed increased dermal mucin and cellularity, with proliferation of spindled fibroblastic cells. Paraprotein was not detected in the serum. Facial sparing, the absence of serum paraprotein and the histopathological findings confirmed the diagnosis of nephrogenic systemic fibrosis. Immunohistochemical stains revealed CD34-positive spindle-shaped cells, and electron microscopy did not detect free gadolinium. Following improvement in renal function and various treatments, his plaques softened, fibrosis slowed and mobility partially improved. Gadolinium magnetic resonance cholangiopancreatography was performed following this improvement. Six weeks later, further progression of nephrogenic systemic fibrosis occurred despite normal renal function.