Deep learning algorithm diagnoses skin cancer as well as seasoned dermatologists
Dermatologist here. Most skin cancer diagnosis is relatively straightforward and if the lesion is suspicious will require a biopsy to establish the subtype of the cancer and plan further treatment. There is no reason why this initial visual diagnosis cannot be performed at the same level as a dermatologist by a machine or indeed by a non-doctor trained intensively for a relative short period to interpret photographs.<p>The difficulty is two-fold. Firstly liability - a dermatologist aims not to miss a single case of melanoma in the tens of thousands of patients seen over their career, if this algorithm is used widely in millions of patients then either the sensitivity will have to be higher and more biopsies performed or there will have to be an acceptable rate of missed diagnosis for melanoma.<p>Secondly, in edge cases such as moles that are slightly atypical. In these scenarios there is no way that I would be comfortable making an assessment from a photograph. Now of course, a machine could also gather further information via methods such as in vivo confocal microscopy but in this case the cost savings are likely to be negligible.