1st Edition Biomedicine and Healthcare Applications
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
Researchers, advanced graduate students, bioengineers, digital therapeutic product developers, and clinicians in the fields of neuroscience, psychiatry, biomedicine, and computer science. Regulators and policy makers
Table of Contents
- Introducing Women’s Brain Project towards a fair and personalized approach to human health in the era of data-driven medicine
Maria Teresa Ferretti, Antonella Santuccione Chadha, Simona Mellino
Section 1. Sex and gender differences and Precision Medicine
- Implications of sex-specific differences on clinical studies of human health
Janet Pinero, Frances-Catherine Quevenco, Laura Furlong, Emre Guney
- Sex and gender differences and Precision Medicine
Nataly Buslón, Sandra Racionero, Atia Cortés
Section 2. Biases in innovative technologies for Biomedicine and Health
4. Bias and fairness in machine learning and artificial intelligence
Davide Cirillo, Maria Jose Rementeria
5. Big Data in healthcare from a sex/gender perspective
Laia Subirats, Gemma Piella
6. Biases in digital biomarkers and Mobile Health
Simona Mellino, Czuee Morey, Colin Rohner
7. Sex and Gender bias in Natural Language Processing
Davide Cirillo, Hila Gonen, Enrico Santus, Alfonso Valencia, Marta R. Costa-jussà, Marta Villegas
8. Sex and gender differences in Invasive and non invasive neurotechnologies
Laura Dubreuil Vall, Tracy Laabs, Harris Eyre, Erin Smith, Silvina Catuara-Solarz
9. Robots and Affective technologies
Section 3. Towards Precision Technology
10. A unified framework for the management of sex and gender biases in Healthcare
Silvina Catuara-Solarz, Giovanni Maffei, Federico Lucchesi, Roberto Confalonieri
11. Privacy Preserving technologies
Ladina Caduff, Cornelia Kutterer, Spyridon Papasotiriou
12. Ethics and Society
Atia Cortes, Nataly Buslón, Liliana Arroyo
Conduct research on sex and gender bias analysis in computational biology and AI research for personalised medicine applications and scientific practice
The study, promoted by the non-profit organization The Women’s Brain Project and supported by the Bioinfo4Women programme at the BSC, has been published today in Nature Digital Medicine.
The application of artificial intelligence (AI) to the biomedical sector is leading us to a better understanding of human diseases, facilitating their prevention, diagnosis, personalised treatments and, in general, precision medicine.
The success of precision medicine largely depends on overcoming a number of challenges, many of which are inherently related to the responsible use of AI in research and healthcare. To meet these ambitious objectives, it is essential to account for the risks of neglecting differences among individuals that reflect the clinical characteristics of diseases and drugs response, such as sex and gender differences.
The researchers involved in this analysis warn about the need for the community in its entirety, including governments and policy makers, to become involved in addressing the ethical issues associated with each stage of the technological development for health.