Real-world evaluation of the use of melanoma risk prediction tools by clinicians in Australia: a mixed-methods study

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Authors: Gillian Reyes-Marcelino, Rehana A Salam, David Espinoza, Caroline G. Watts, Julia Steinberg, Yoon-Jung Kang, Linda K. Martin, Pascale Guitera, Serigne N. Lo, Alexander H. R. Varey, Richard A. Scolyer, Georgina Venetia Long
+2 more Amelia K. Smit, Anne Elizabeth Cust
Cited by: 0
References: 7

Abstract

Melanoma risk prediction tools have been developed to assist with estimating a person’s risk of developing a primary melanoma, yet their integration into clinical practice and their influence on decision making remains poorly understood. Using a mixed-methods approach, we aimed to evaluate the use of two online melanoma risk prediction tools – one for estimating risk of a first primary melanoma and the other for estimating risk of a subsequent primary melanoma – by clinicians in Australia. We found that the first primary and subsequent primary melanoma risk prediction tools were accepted by clinicians for use in routine practice; however, variability in their use and challenges in interpreting and applying risk results may hinder their full potential.

Fields of Study & Subjects

Cutaneous Melanoma Detection and Management Artificial Intelligence in Healthcare and Education AI in cancer detection

Publication details

Journal / source
British Journal of Dermatology
Volume
194
Issue
5
Pages
957–959
Publication date
7 January 2026
Language
EN