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

Caffery, Liam J; Janda, Monika; Miller, Robert E.; Abbott, Lisa M.; Arnold, Chris; Caccetta, Tony; Guitera, Pascale; Shumack, Stephen; Fernández‐Peñas, Pablo; Mar, Victoria; Soyer, H. Peter

doi:10.1111/ajd.13946

Cited by 6 publications

(2 citation statements)

References 36 publications

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“…2 Despite immense potential, AI is still an evolving technology with many questions yet to be clarified before it can be safely integrated into clinical pathways. 1,3,4 The Achille's heel of AI is its propensity to 'overfit' to the data set used to train the algorithm. 1,5 This phenomenon affects generalizability, meaning that the algorithm adapts poorly to variations in data capture or characteristics.…”

Section: Introductionmentioning

confidence: 99%

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

Ingvar,

Oloruntoba,

Sashindranath

et al. 2024

Aust J Dermatology

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Background/ObjectivesArtificial 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.MethodsCommon 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.ResultsThere 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.ConclusionsThis 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.

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Section: Introductionmentioning

confidence: 99%

“…In these fields, AI tackles tasks such as disease screening, diagnostic classification, segmentation and pathology detection 2 . Despite immense potential, AI is still an evolving technology with many questions yet to be clarified before it can be safely integrated into clinical pathways 1,3,4 …”

Section: Introductionmentioning

confidence: 99%

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

Ingvar,

Oloruntoba,

Sashindranath

et al. 2024

Aust J Dermatology

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Challenges of artificial intelligence in medicine and dermatology

Grzybowski,

Jin,

2024

Clinics in Dermatology

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Ethical considerations for artificial intelligence in dermatology: a scoping review

Gordon,

Trager,

Kontos

et al. 2024

British Journal of Dermatology

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The field of dermatology is experiencing the rapid deployment of artificial intelligence (AI), from mobile applications for skin cancer detection to large language models like ChatGPT that can answer generalist or specialist questions about skin diagnoses. With these new applications, ethical concerns have emerged. In this scoping review, we aim to identify the applications of AI to the field of dermatology and to understand their ethical implications. We utilized a multifaceted search approach, searching PubMed, Medline, Cochrane, and Google Scholar for primary literature according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) Extension for Scoping Reviews. Our advanced query included terms related to dermatology, artificial intelligence, and ethical considerations. Our search yielded a total of 202 papers. After initial screening, 68 studies were included. Thirty-two related to clinical image analysis and raised ethical concerns for misdiagnosis, data security, violations of privacy, and replacement of dermatologist jobs. Seventeen discussed limited skin of color representation in datasets leading to potential misdiagnosis in the general population. Nine articles about teledermatology raised ethical concerns, including the exacerbation of health disparities, lack of standardized regulations, informed consent for AI use, and privacy challenges. Seven addressed inaccuracies of responses of large language models. Seven examined attitudes and trust towards AI, with most patients requesting supplemental assessment by a physician to ensure reliability and accountability. Benefits of artificial intelligence integration into clinical practice include increased patient access, improved clinical decision making, efficiency, and many others. However, safeguards must be implemented to ensure ethical applications of artificial intelligence.

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

Cited by 6 publications

References 36 publications

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

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

Challenges of artificial intelligence in medicine and dermatology

Ethical considerations for artificial intelligence in dermatology: a scoping review

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