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

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

doi:10.3389/fmed.2024.1388422

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

Front Med (Lausanne). 2024 May 2:11:1388422. doi: 10.3389/fmed.2024.1388422. eCollection 2024.

Authors

Lucy Thomas¹, Chris Hyde², Daniel Mullarkey³, Jack Greenhagh³, Dilraj Kalsi³, Justin Ko⁴

Affiliations

¹ Chelsea and Westminster Hospital NHS Foundation Trust, London, United Kingdom.
² Exeter Test Group, Department of Health and Community Science, University of Exeter Medical School, Exeter, United Kingdom.
³ Skin Analytics Ltd., London, United Kingdom.
⁴ Department of Dermatology, School of Medicine, Stanford University, Stanford, CA, United States.

No abstract available

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

Publication types

Comment

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The authors declare that this study received funding from Skin Analytics, London, UK. The funder had the following involvement in the study: study design, data collection and analysis, decision to publish, and preparation of the manuscript.