About this Research Topic
The increasing presence of artificial intelligence (AI) worldwide, has led to numerous attempts at leveraging such technology for challenging healthcare problems. In the field of dermatology, the most progress thus far, has taken place in the field of melanoma diagnosis.
Multiple original articles and several reviews have been published demonstrating that camera systems with algorithmic processing abilities may be able to differentiate between nevi and malignant lesions with similar accuracy to certified dermatologists. Recognizing, however, that the field of dermatology is much broader than melanoma diagnosis, there is now a considerable number of additional topics under study. For example, several studies are emerging on methods to better classify and understand psoriasis, predict leg/foot ulcer healing and differentiate between various types of myositis using ultrasound images rather than biopsies. Others are attempting to predicting skin sensitization to putative allergens before the exposure can occur. Yet at this time, most efforts are still pioneering, and practice change is far from being actualized. This is due to a lack of robust models, clinical trials and cost-effectiveness studies.
Nevertheless, it reasonable to assume that these hurdles will be overcome with time. Future research is thus headed in two directions. First, towards improving prototypical models to create more objective and accurate tools. Second, towards understanding this disruptive technology capabilities from a practical perspective to help guide and shape the future for medical care providers and recipients in dermatology.
To be at the frontier of this exciting research, a deeper delve is imminently due. We have thus created a special topic and encourage the authors to submit their original work and reviews discussing artificial intelligence use in dermatology.
Multiple original articles and several reviews have been published demonstrating that camera systems with algorithmic processing abilities may be able to differentiate between nevi and malignant lesions with similar accuracy to certified dermatologists. Recognizing, however, that the field of dermatology is much broader than melanoma diagnosis, there is now a considerable number of additional topics under study. For example, several studies are emerging on methods to better classify and understand psoriasis, predict leg/foot ulcer healing and differentiate between various types of myositis using ultrasound images rather than biopsies. Others are attempting to predicting skin sensitization to putative allergens before the exposure can occur. Yet at this time, most efforts are still pioneering, and practice change is far from being actualized. This is due to a lack of robust models, clinical trials and cost-effectiveness studies.
Nevertheless, it reasonable to assume that these hurdles will be overcome with time. Future research is thus headed in two directions. First, towards improving prototypical models to create more objective and accurate tools. Second, towards understanding this disruptive technology capabilities from a practical perspective to help guide and shape the future for medical care providers and recipients in dermatology.
To be at the frontier of this exciting research, a deeper delve is imminently due. We have thus created a special topic and encourage the authors to submit their original work and reviews discussing artificial intelligence use in dermatology.
Keywords: dermatology, artificial intelligence, melanoma, nevi, myositis, ulcers, wound healing
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