AUTHOR=Khatun Nazma , Spinelli Gabriella , Colecchia Federico 

TITLE=Technology innovation to reduce health inequality in skin diagnosis and to improve patient outcomes for people of color: a thematic literature review and future research agenda

JOURNAL=Frontiers in Artificial Intelligence

VOLUME=Volume 7 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2024.1394386

DOI=10.3389/frai.2024.1394386

ISSN=2624-8212

ABSTRACT=The health inequalities experienced by ethnic minorities have been a persistent and global phenomenon. The diagnosis of different types of skin conditions, e.g., melanoma, among people of colour is one of such health domains where misdiagnosis take place and may lead to life-threatening consequences. Although Caucasians are more likely to be diagnosed with melanoma, African Americans are four times more likely to present stage IV melanoma, this is due to delayed diagnosis’s (Mahendraraj et al., 2017). However, it is essential to recognise that additional factors such as socioeconomic status or limited access to healthcare services can be contributing factors. African Americans are also 1.5 times more likely to die from melanoma than Caucasians, with the 5-year survival rates for African Americans being significantly lower than Caucasians (72.2% vs 89.6%) (Mahendraraj et al., 2017). This is a complex problem compounded by several factors: ill-prepared medical practitioners, lack of awareness of melanoma and other skin conditions among POC, lack of information and medical resources for practitioners’ continuous development, under-representation of POC in research, POC being a notoriously hard to reach group, and ‘whitewashed’ medical school curriculums.  Whilst digital technology and the application of machine learning bring new hope for the reduction of health inequality, the deployment of artificial intelligence in healthcare carries risks that may amplify the health disparities experienced by POC, whilst digital technology may provide a false sense of participation. For instance, Derm Assist, a skin diagnosis phone application which is under development, has already been criticised for using limited participants of colour within its study. This paper focuses on understanding the problem of misdiagnosing skin conditions in people of colour and exploring the progress and innovations that have been experimented with, to pave the way to the possible application of big data analytics, artificial intelligence, and user-centred technology to reduce health inequalities among people of colour.