Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare

By D. Cirillo, Silvina Catuara-Solarz, Czuee Morey, Emre Guney, L. Subirats, Simona Mellino, A. Gigante, A. Valencia, M. Rementeria, Antonella Santuccione Chadha and 1 more. npj Digital Medicine, 2020

418 citations recorded by the source corpus.

Precision Medicine implies a deep understanding of inter-individual differences in health and disease that are due to genetic and environmental factors. To acquire such understanding there is a need for the implementation of different types of technologies based on artificial intelligence (AI) that enable the identification of biomedically relevant patterns, facilitating progress towards individually tailored preventative and therapeutic interventions. Despite the significant scientific advances achieved so far, most of the currently used biomedical AI technologies do not account for bias detection. Furthermore, the design of the majority of algorithms ignore the sex and gender dimension and its contribution to health and disease differences among individuals. Failure in accounting for these differences will generate sub-optimal results and produce mistakes as well as discriminatory outcomes. In this review we examine the current sex and gender gaps in a subset of biomedical technologies used in relation to Precision Medicine. In addition, we provide recommendations to optimize their utilization to improve the global health and disease landscape and decrease inequalities.

Subfields: Gender Studies, Public Health, Environmental and Occupational Health, Reproductive Medicine

View Paper: https://www.semanticscholar.org/paper/aa64d955454464ef5d921cc9df6682ff4921b2e3

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In Public Health, Environmental and Occupational Health

1. M. de Choudhury
2. Dilip Jeste

Citations in the corpus

• Artificial intelligence in histopathology: enhancing cancer research and clinical oncology (2022, background): “This is problematic because it can propagate sexism, racism and other types of discrimination inherent in many medical datasets 131,132 .”
• Artificial Intelligence for Mental Healthcare: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom (2021, background): “J Ungoverned AI might perpetuate such bias through furthering social inequities or demonstrating prejudices by arriving at different conclusions for individuals (e.g., based on race or gender) without any rational basis [see (120)].”
• Bias in artificial intelligence algorithms and recommendations for mitigation (2023, background): “Though gender, race, and ethnicity are frequently identified as potential sources of bias, other factors may contribute to bias such as age, socioeconomic differences, and geography [58].”
• Machine learning and deep learning—A review for ecologists (2022, background): “For example, it is possible to algorithmically detect biases (Cirillo et al., 2020 ) and correct the models accordingly (e.g. Alvi et al., 2018 ; Kim et al., 2019 ) by using fairness metrics to guide training so that underrepresented groups are not neglected (e.g. Liu & Vicente, 2021 ).”
• Diagnostic Accuracy of Differential-Diagnosis Lists Generated by Generative Pretrained Transformer 3 Chatbot for Clinical Vignettes with Common Chief Complaints: A Pilot Study (2023, background): “For example, gender [37,38], racial [17], and religious [17] biases have been reported.”