H. Peter Soyer

A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology

2021 · 269 Zit. · Scientific Reports

Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts

2021 · 243 Zit. · European Journal of Cancer

Checklist for Evaluation of Image-Based Artificial Intelligence Reports in Dermatology

2021 · 143 Zit. · JAMA Dermatology

Computer algorithms show potential for improving dermatologists' accuracy to diagnose cutaneous melanoma: Results of the International Skin Imaging Collaboration 2017

2019 · 102 Zit. · Journal of the American Academy of Dermatology

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

2022 · 17 Zit. · Australasian Journal of Dermatology

Effect of patient‐contextual skin images in human‐ and artificial intelligence‐based diagnosis of melanoma: Results from the 2020 <scp>SIIM</scp>‐<scp>ISIC</scp> melanoma classification challenge

2024 · 6 Zit. · Journal of the European Academy of Dermatology and Venereology

What Are Patients’ Perceptions and Attitudes Regarding the Use of Artificial Intelligence in Skin Cancer Screening and Diagnosis? Narrative Review

2025 · 5 Zit. · Journal of Investigative Dermatology

Attitudes Towards the Use of Artificial Intelligence in Dermatology: A Survey of Australian Dermatologists

2025 · 5 Zit. · Australasian Journal of Dermatology

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

2024 · 5 Zit. · Australasian Journal of Dermatology

Multi‐task <scp>AI</scp> models in dermatology: Overcoming critical clinical translation challenges for enhanced skin lesion diagnosis

2025 · 2 Zit. · Journal of the European Academy of Dermatology and Venereology

Informing a Position Statement on the Use of Large Language Models and <scp>AI</scp> Scribes in Dermatology in Australia

2025 · 2 Zit. · Australasian Journal of Dermatology

Exploring the Views of Dermatologists, General Practitioners, and Melanographers on the Use of AI Tools in the Context of Good Decision-Making When Detecting Melanoma: Qualitative Interview Study

2025 · 2 Zit. · JMIR Dermatology

421 Pathologist consensus for diagnostic labels is crucial in the developement of artificial intelligence training data in dermatology

2024 · 0 Zit. · Journal of Investigative Dermatology

Exploring the Views of Dermatologists, General Practitioners, and Melanographers on the Use of AI Tools in the Context of Good Decision-Making When Detecting Melanoma: Qualitative Interview Study (Preprint)

2024 · 0 Zit.