Sex and Gender Bias in Technology and Artificial Intelligence: Biomedicine and Healthcare Applications details the integration of sex and gender as critical factors in innovative technologies (artificial intelligence, digital medicine, natural language processing, robotics) for biomedicine and healthcare applications. By systematically reviewing existing scientific literature, a multidisciplinary group of international experts analyze diverse aspects of the complex relationship between sex and gender, health and technology, providing a perspective overview of the pressing need of an ethically-informed science. The reader is guided through the latest implementations and insights in technological areas of accelerated growth, putting forward the neglected and overlooked aspects of sex and gender in biomedical research and healthcare solutions that leverage artificial intelligence, biosensors, and personalized medicine approaches to predict and prevent disease outcomes. The reader comes away with a critical understanding of this fundamental issue for the sake of better future technologies and more effective clinical approaches.
- First comprehensive title addressing the topic of sex and gender biases and artificial intelligence applications to biomedical research and healthcare
- Co-published by the Women's Brain Project, a leading non-profit organization in this area
- Guides the reader through important topics like the Generation of Clinical Data, Clinical Trials, Big Data Analytics, Digital Biomarkers, Natural Language Processing
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science & Techn.
Illustrationen
Approx. 150 illustrations (150 in full color)
Dateigröße
ISBN-13
978-0-12-821393-3 (9780128213933)
Schweitzer Klassifikation
1. Introducing Women's Brain Project towards a fair and personalized approach to human health in the era of data-driven medicine
Section 1. Sex and gender differences and Precision Medicine2. Implications of sex-specific differences on clinical studies of human health3. Sex and gender differences and Precision Medicine
Section 2. Biases in innovative technologies for Biomedicine and Health4. Bias and fairness in machine learning and artificial intelligence5. Big Data in healthcare from a sex/gender perspective6. Biases in digital biomarkers and Mobile Health7. Sex and Gender bias in Natural Language Processing8. Sex and gender differences in Invasive and non invasive neurotechnologies9. Robots and Affective technologies
Section 3. Towards Precision Technology10. A unified framework for the management of sex and gender biases in Healthcare11. Privacy Preserving technologies12. Ethics and Society
