Skin cancer is something that affects us all. Accounting for one third of all cancer cases, the number of new diagnoses is rising at an alarming rate, increasing by 7.7% in 2019 alone.1 In addition to being the most common form of cancer, it is also one of the most treatable. Melanoma, the deadliest form of skin cancer has a survival rate of 98% when detected in its earliest stages, but when left unnoticed this drops to only 16%.2 In order for patients to receive the best treatment outcomes, they must have access to efficient, quality, and effective services.
Unfortunately, due to a rising dermatologist shortage, this fails to be the case for the majority of patients across North America, where average wait times to see a professional are more than two months. As a result, a growing number of medical professionals are offering dermatology services to meet this rapidly increasing demand.
Identifying the nature of skin lesions heavily relies upon the expertise of the care provider. However, due to the complexity of skin image analysis and classification, this process often proves challenging for even the most seasoned experts, consequently creating a large number of unnecessary biopsy specimens. The economic burden and physical trauma for patients to undergo invasive surgeries combined with the low rate of false positives against skin cancer cases make it necessary to adopt a new generation of tools to support accurate, evidence-based clinical decisions. How can artificial intelligence (AI) support this growing demand?
Harnessing the power of technology represents an enormous advancement towards improved analysis and diagnosis of pigmented skin lesions. AI technology has the capacity to revolutionize the way medical professionals offer best healthcare outcomes to patients. Machine learning capabilities become strategic technical allies that provide highly accurate decision-making support based on the accumulated analysis of millions of previously classified cases.
The implementation potential of AI efforts in dermatology are better exposed in global initiatives aimed to the close collaboration of key stakeholders in the field. The power and specificity derived from AI algorithms can be studied and promoted through the cumulative participation of different research leaders in the area as encouraged by the ISIC challenge for image classification.
-The MetaOptima Team
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Topics: Dermatology AI Intelligent Dermatology Services Artificial Intelligence in Dermatology AI In Healthcare EMR Dermatology EMR Dermatology Software Intelligent Dermatology Software Dermatology EMR Software EMR Software Cloud Based EMR ISIC2018 Intelligent Dermatology White Paper