The algorithm was trained with nearly 130,000 images representing more than 2,000 different diseases with an associated disease label, allowing the system to overcome variations in angle, lighting, and zoom. The algorithm was then tested against 1,942 images of skin that were digitally annotated with biopsy-proven diagnoses of skin cancer. Overall, the algorithm identified the vast majority of cancer cases with accuracy rates that were similar to expert clinical dermatologists.
However, Susan Swetter, professor of dermatology and director of the Pigmented Lesion and Melanoma Program at the Stanford Cancer Institute notes that “rigorous prospective validation of the algorithm is necessary before it can be implemented in clinical practice, by practitioners and patients alike.”
Read more here