Lead author Ernest Lee, MD, PhD, and colleagues found many studies in the recent literature focused on image analysis and classification of skin lesions—no surprise since digital photography is by now ubiquitous in the field.
Here they comment that machine learning is “a natural fit for translation into dermatology because dermatology is a specialty that is heavily reliant on visual evaluation and pattern recognition.”
However, the researchers also found machine learning is being applied to everything from studying the genetic basis of skin diseases to identifying associations between comorbidities, and to designing and predicting patient responses to drug therapies.
The simultaneous rise of machine learning and next-generation sequencing in particular “represents a golden opportunity to advance precision dermatology, and multidisciplinary collaborations between machine learning experts, biologists and dermatologists will be required to expand the scope of this research,” Lee and co-authors write.
Credit: Google News
