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.

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 color is one of such health domains where misdiagnosis can take place, potentially leading 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 due to delayed diagnosis. It is essential to recognize that additional factors such as socioeconomic status and 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 5-year survival rates for African Americans significantly lower than for Caucasians (72.2% vs. 89.6%). This is a complex problem compounded by several factors: ill-prepared medical practitioners, lack of awareness of melanoma and other skin conditions among people of colour, lack of information and medical resources for practitioners' continuous development, under-representation of people of colour in research, POC being a notoriously hard to reach group, and 'whitewashed' medical school curricula. Whilst digital technology can 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 people of color, 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 criticized for relying on data from a limited number of people of color. This paper focuses on understanding the problem of misdiagnosing skin conditions in people of color 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 color.

Using immersive technologies to facilitate location scouting in audiovisual media production: a user requirements study and proposed framework.

In common with many industries, the audiovisual sector is likely to be transformed by real-time graphic engines in combination with immersive technologies such as Virtual Reality and Augmented Reality. This technological mix enables what is presently known as Virtual Production, introducing a new process to the audiovisual professional, capable of fostering creativity, collaboration, and decision making, while at the same time increasing efficiency. The potential for Virtual Production to transform workflows and creative processes within large-scale productions in the audiovisual sector is significant and includes the prospective introduction of new capabilities for remote co-creation of content. However, barriers to democratisation need to be overcome, particularly in relation to adoption of the technology by small and medium independent productions that generally have limited access to resources and technical knowledge. Following an extensive study of the current literature and involvement in the research of professionals working in the field through online interviews, this article documents a first step towards filling this gap by investigating Virtual Production adoption scenarios for small and medium independent productions. The primary aim is to design intuitive, engaging, and effective solutions to address the needs of Directors, Cinematographers, and Producers. Thanks to the valuable time and experience of industry professionals, this study has gathered user requirements for a Virtual Production design solution capable of facilitating location scouting and pre-production. A novel framework for remote exploration of target locations within immersive environments, co-created with relevant stakeholders, is presented to lay the foundations of future co-design work.

Tissue-specific regulatory network extractor (TS-REX): a database and software resource for the tissue and cell type-specific investigation of transcription factor-gene networks.

The prediction of transcription factor binding sites in genomic sequences is in principle very useful to identify upstream regulatory factors. However, when applying this concept to genomes of multicellular organisms such as mammals, one has to deal with a large number of false positive predictions since many transcription factor genes are only expressed in specific tissues or cell types. We developed TS-REX, a database/software system that supports the analysis of tissue and cell type-specific transcription factor-gene networks based on expressed sequence tag abundance of transcription factor-encoding genes in UniGene EST libraries. The use of expression levels of transcription factor-encoding genes according to hierarchical anatomical classifications covering different tissues and cell types makes it possible to filter out irrelevant binding site predictions and to identify candidates of potential functional importance for further experimental testing. TS-REX covers ESTs from H. sapiens and M. musculus, and allows the characterization of both presence and specificity of transcription factors in user-specified tissues or cell types. The software allows users to interactively visualize transcription factor-gene networks, as well as to export data for further processing. TS-REX was applied to predict regulators of Polycomb group genes in six human tumor tissues and in human embryonic stem cells.