Author ORCID Identifier
0009-0000-2447-724X
Document Type
Conference Paper
Disciplines
Statistics
Abstract
Women’s healthcare is a complex, multifaceted issue with both historic and implicit biases, along with biological differences between men and women. With the advancement of AI tools in healthcare and the potential for biased data to create biased models, it is vital to consider how women are represented in data. Previously conducted semi-structured semantic interviews with clinicians were analysed via Braun and Clark’s method of thematic analysis. The analysis of these interviews yielded the following themes: Gender Influencing Health, Pregnancy, Social Factors, General Health, Treatment, Training, and Research. These themes highlight that context is key to understanding the biases in women’s health and that this context is critical when developing AI models for healthcare. However, whether these factors are present in real world medical data is still unknown along with the extent to which these factors impact women’s health. Pathology is a medical discipline which analyses bodily fluids, waste, tissues and organs with the purpose of gathering information on a patients health. Pathology data obtained from an Irish Hospital contains many of the features needed to analyse the presence and impact of these features.
DOI
https://doi.org/10.21427/7emd-0p57
Recommended Citation
Heaney, Andrea; Hickey, Eugene; and Murphy, Emma, "Pathology’s Place in Understanding the Bias and Inequalities in Women’s Healthcare" (2025). SAML-25 Workshop on Statistical and Machine Learning. 23.
https://arrow.tudublin.ie/saml/23
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Publication Details
Statistical and Machine Learning: Methods and Applications (SAML-25) on June 5th and 6th, 2025.
doi:10.21427/7emd-0p57