LesionMap: A Method and Tool for the Semantic Annotation of Dermatological Lesions for Documentation and Machine Learning

DICODerma: A Practical Approach for Metadata Management of Images in Dermatology

Clinical images are vital for diagnosing and monitoring skin diseases, and their importance has increased with the growing popularity of machine learning. Lack of standards has stifled innovation in dermatological imaging, unlike other image-intensive specialties such as radiology. We investigate the meta-requirements for utilizing the popular DICOM standard for metadata management of images in dermatology. We propose practical design solutions and provide open-source tools to integrate dermatologists’ workflow with enterprise imaging systems. Using the tool, dermatologists can tag, search, organize and convert clinical images to the DICOM format. We believe that our less disruptive approach will improve the adoption of standards in the specialty.

Artificial Intelligence in Dermatology: A Practical Introduction to a Paradigm Shift

Artificial Intelligence (AI) has surpassed dermatologists in skin cancer detection, but dermatology still lags behind radiology in its broader adoption. Building and using AI applications are becoming increasingly accessible. However, complex use cases may still require specialized expertise for design and deployment. AI has many applications in dermatology ranging from fundamental research, diagnostics, therapeutics, and cosmetic dermatology. The lack of standardization of images and privacy concerns are the foremost challenges stifling AI adoption. Dermatologists have a significant role to play in standardized data collection, curating data for machine learning, clinically validating AI solutions, and ultimately adopting this paradigm shift that is changing the way we practice.