Will Big Data and personalized medicine do the gender dimension justice?

Autor: Andrea Iannone, Antonio Carnevale, Elena Sartini, Emanuela A. Tangari
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Ai & Society
AI & SOCIETY
ISSN: 1435-5655
0951-5666
Popis: Over the last decade, humans have produced each year as much data as were produced throughout the entire history of humankind. These data, in quantities that exceed current analytical capabilities, have been described as “the new oil,” an incomparable source of value. This is true for healthcare, as well. Conducting analyses of large, diverse, medical datasets promises the detection of previously unnoticed clinical correlations and new diagnostic or even therapeutic possibilities. However, using Big Data poses several problems, especially in terms of representing the uniqueness of each patient and expressing the differences between individuals, primarily gender and sex differences. The first two sections of the paper provide a definition of “Big Data” and illustrate the uses of Big Data in medicine. Subsequently, the paper explores the struggle to represent exhaustively the uniqueness of the patient through Big Data is highlighted prior to a deeper investigation of the digital representation of gender in personalized medicine. The final part of the paper put forward a series of recommendations for better approaching the complexity of gender in medical and clinical research involving Big Data for the creation or enhancement of personalized medicine services. Supplementary Information The online version contains supplementary material available at 10.1007/s00146-021-01234-9.
Databáze: OpenAIRE
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