By combining those tools with machine learning, their goal was to see if the combination would work as a noninvasive and more objective in vivo diagnostic tool.

The researchers suggest that these advanced, pretrained machine learning models could expand the reach of machine learning-based cancer diagnosis to resource-limited settings.

In this study, researchers used machine learning and combination theory to distil 22 clinical features down to the seven most important that predict if a skin lesion might be suspicious or not.

Devices that use artificial intelligence to evaluate skin lesions are beginning to emerge, but AI has some hurdles to overcome before it's widely accepted in dermatology.

New screening technology makes it easier to detect, track skin cancers According to the American Cancer Society, more than 100,000 people will be diagnosed with melanoma this year. The good news ...