It’s the ethical imperative of medical providers and researchers to improve the health outcomes of either their patients or the general public.
The connection between sun exposure and skin cancers, such as melanoma, is well acknowledged. However, some people are at higher risk of melanoma than others, meaning they may require less sun exposure to cause the DNA damage which can lead to skin cancers.
A recent nation-wide survey of Australian Dermatologists has provided insight into the current use, confidence, attitudes, and education preferences for genetic testing in dermatology practice.
In the last decade the application of artificial intelligence (AI) algorithms in dermatology to classify skin lesions, particularly melanoma, has advanced rapidly. Large international computer skin image analysis challenges have successfully drawn attention to the potential for AI to aid the detection of skin cancers.
In recent times, data has become one of the most precious resources in both business and science. For projects such as iToBoS, which aims to utilize deep learning in the global fight against melanoma, the veracity, validity and volume of data is essential.
Due to the ubiquity of AI systems in our society, awareness has been raised for the need of neural networks and their predictions to be transparent and explainable.
DICOM (Digital Imaging and Communications in Medicine) files are a standard for storing and transmitting medical imaging information. They are more commonly used in various radiology modalities, such as MRI or CT scans, as they can contain multiple frames of an image in a single file, thus allowing to store a 3D image formed by slices.