Response: Commentary: Real-world post-deployment performance of a novel machine learning-based digital health technology for skin lesion assessment and suggestions for post-market surveillance.

Thomas, Lucy; Hyde, Chris; Mullarkey, Daniel; Greenhagh, Jack; Kalsi, Dilraj; Ko, Justin

Thomas, Lucy; Hyde, Chris; Mullarkey, Daniel; Greenhagh, Jack; Kalsi, Dilraj; Ko, Justin.

Afiliación

Thomas L; Chelsea and Westminster Hospital NHS Foundation Trust, London, United Kingdom.
Hyde C; Exeter Test Group, Department of Health and Community Science, University of Exeter Medical School, Exeter, United Kingdom.
Mullarkey D; Skin Analytics Ltd., London, United Kingdom.
Greenhagh J; Skin Analytics Ltd., London, United Kingdom.
Kalsi D; Skin Analytics Ltd., London, United Kingdom.
Ko J; Department of Dermatology, School of Medicine, Stanford University, Stanford, CA, United States.

Front Med (Lausanne) ; 11: 1388422, 2024.

Article en En | MEDLINE | ID: mdl-38756949

Palabras clave

AI as a medical device; AI for skin cancer; DERM; Skin Analytics; artificial intelligence; deep ensemble for the recognition of malignancy; skin cancer

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Med (Lausanne) Año: 2024 Tipo del documento: Article País de afiliación: Reino Unido Pais de publicación: Suiza

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