The images and accompanying data available for training artificial intelligence (AI) to spot skin cancer are insufficient and include very few images of darker skin, according to new research.
AI is increasingly being used in medicine as it can make diagnosis of diseases like skin cancer quicker and more effective. However, AI needs to be ‘trained’ by looking at data and images from a large number of patients where the diagnosis has already been established and so an AI program depends heavily upon the information it is trained on.
Researchers say there is an urgent need for better sets of data on skin cancers and other skin lesions which contain information on who is represented in the datasets.
The research was presented by Dr David Wen from the University of Oxford, UK. He said: «AI programs hold a lot of potential for diagnosing skin cancer because it can look at pictures and quickly and cost-effectively evaluate any worrying spots on the skin. However, it’s important to know about the images and patients used to develop programs, as these influence which groups of people the programs will be most effective for in real-life settings. Research has shown that programs trained on images taken from people with lighter skin types only might not be as accurate for people with darker skin, and vice versa.»
Dr Wen and his colleagues carried out the first ever review of all freely accessible sets of data on skin lesions around the world. They found 21 sets including more than 100,000 pictures.
Diagnosis of skin cancer normally requires a photo of the worrying lesion as well as a picture taken with a special hand-held magnifier, called a dermatoscope, but only two of the 21 datasets included images taken with both of these methods. The datasets were also missing other important information, such as how images were chosen to be included, and evidence of ethical approval or patient consent.
Story Source: Materials provided by National Cancer Research Institute. Original written by Rachel Laurence. Note: Content may be edited for style and length.