Towards gender equity in artificial intelligence and machine learning applications in dermatology

Abstract

There has been increased excitement around the use of machine learning (ML) and artificial intelligence (AI) in dermatology for the diagnosis of skin cancers and assessment of other dermatologic conditions. As these technologies continue to expand, it is essential to ensure they do not create or widen sex- and gender-based disparities in care. While desirable bias may result from the explicit inclusion of sex or gender in diagnostic criteria of diseases with gender-based differences, undesirable biases can result from usage of datasets with an underrepresentation of certain groups. We believe that sex and gender differences should be taken into consideration in ML/AI algorithms in dermatology because there are important differences in the epidemiology and clinical presentation of dermatologic conditions including skin cancers, sex-specific cancers, and autoimmune conditions. We present recommendations for ensuring sex and gender equity in the development of ML/AI tools in dermatology to increase desirable bias and avoid undesirable bias.