  We examine progress in the use of AI for detecting skin lesions, with
particular emphasis on the erythema migrans rash of acute Lyme disease, and
other lesions, such as those from conditions like herpes zoster (shingles),
tinea corporis, erythema multiforme, cellulitis, insect bites, or tick bites.
We discuss important challenges for these applications, in particular the
problems of AI bias regarding the lack of skin images in dark skinned
individuals, being able to accurately detect, delineate, and segment lesions or
regions of interest compared to normal skin in images, and low shot learning
(addressing classification with a paucity of training images). Solving these
problems ranges from being highly desirable requirements -- e.g. for
delineation, which may be useful to disambiguate between similar types of
lesions, and perform improved diagnostics -- or required, as is the case for AI
de-biasing, to allow for the deployment of fair AI techniques in the clinic for
skin lesion analysis. For the problem of low shot learning in particular, we
report skin analysis algorithms that gracefully degrade and still perform well
at low shots, when compared to baseline algorithms: when using a little as 10
training exemplars per class, the baseline DL algorithm performance
significantly degrades, with accuracy of 56.41%, close to chance, whereas the
best performing low shot algorithm yields an accuracy of 85.26%.
