Overcoming the Data Gap for the Remote Diagnosis of Skin Cancer

The use of AI algorithms for categorizing medical images has become very popular and critical in the diagnosis of various diseases. Current computer-aided diagnosis (CAD) systems are hugely dependent on good quality, well-annotated data captured by professional medical equipment. In many remote areas, a lack of medical equipment and medical specialists that are respectively necessary for producing good quality data and annotating data, have caused a data gap and has resulted in no possibility of using CAD systems in those areas. Here, I point out other sources of data by previewing a recently published dataset that could help resolve this worldwide issue.