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Use of artificial intelligence in skin cancer diagnosis and management

Miki Wada, ZongYuan Ge, Stephen J Gilmore and Victoria J Mar
Med J Aust 2020; 213 (6): . || doi: 10.5694/mja2.50759
Published online: 7 September 2020

The challenge now is how to implement artificial intelligence technology safely into clinical practice

Artificial intelligence is a branch of computer science that, in broad terms, deals with either decision making or classification. The aim of artificial intelligence is to surpass human cognitive functioning such that automated decisions can be made. Machine learning — an application of artificial intelligence — is commonly used in image recognition. In general, the machine, or algorithm, learns from exposure to a large dataset. Once learning has taken place, the algorithm can be applied to unseen data. The potential advantages of this approach in health care are clear: machines can learn from very large datasets in relatively short time frames and can apply themselves to new data without fatigue or intra‐observer replication error.

  • Miki Wada1
  • ZongYuan Ge1
  • Stephen J Gilmore2
  • Victoria J Mar1,3

  • 1 Monash University, Melbourne, VIC
  • 2 Skin Health Institute, Melbourne, VIC
  • 3 Victorian Melanoma Service, Alfred Hospital, Melbourne, VIC

Correspondence: victoria.mar@monash.edu

Acknowledgements: 

Victoria Mar is supported by a National Health and Medical Research Council Early Career Fellowship and has an Australian Cancer Research Foundation infrastructure grant for the establishment of the Australian Centre of Excellence for Melanoma Imaging and Diagnosis, a 3D total body imaging network (Vectra, Canfield Scientific). Victoria Mar and ZongYuan Ge have received a Victorian Medical Research Acceleration Fund grant matched 1:1 by industry funding (MoleMap). We thank Scott Menzies and Yariv Levinson for sharing their knowledge in relation to regulatory requirements and processes.

Competing interests:

No relevant disclosures.

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