AI-Driven Analysis of Gender Equity in Femtech Innovations

Generative AI, particularly large language models (LLMs), is transforming various fields, including the analysis of social mechanisms and processes. LLMs excel in tasks like machine translation, topic modeling, and text summarization, providing richer, context-aware insights in social science research. In translation tasks, LLMs handle complex linguistic structures, capturing context and nuances better than traditional models. In topic modeling, they offer more accurate theme identification, enhancing the analysis of large datasets. This study leverages LLMs to explore gender equity in sexual and FemTech innovations. While FemTech, enhances bodily autonomy and offers personalized health solutions, it may also exacerbate gender inequities due to biased data and lack of diversity in development teams. The study examines how FemTech might overlook gender-related issues and hinder transformative change in SRH services

To explore these dynamics in SRH, this study employs LLMs to analyze user reviews of FemTech-related applications from the Google Play and Apple App Store. The reviews offer rich data sources, capturing user experiences, concerns, and sentiments. By integrating Natural Language Processing (NLP) techniques—such as topic modeling, sentiment analysis, and named entity recognition—we categorize and interpret the textual content of these reviews. Additionally, aspect-based sentiment analysis was used to explore user opinions on specific features of the apps. Through these AI-driven methodologies, we aim to uncover how SRH innovations address gender equity and meet user needs, while also identifying areas where they might hinder progress toward equitable healthcare. Compared to traditional methods, LLMs offer significant advantages in computational social science by automating large-scale text analysis and providing deeper insights from unstructured data. Their ability to capture context, process nuanced language, and adapt to various analytical tasks enhances both the efficiency and accuracy of research, making them powerful tools for addressing complex social issues like gender equity in healthcare.