Artificial intelligence now revolutionizes how we explore life by fueling a renaissance in the field of biology. This new era encompasses rapid scientific advancement, increased specialization, and a push for integration across disciplines. New technologies not only expand the boundaries of what we can study but also demand closer collaboration between experimental and computational science.
Whether wielding pipettes at the bench or orchestrating complex models at the keyboard, women stand at the forefront of this transformative era, actively pushing for increased specialization and more collaboration in biology. Across institutions, startups, and global research communities, women play pioneering roles in this dynamic and interconnected frontier.
Rewiring the Pipeline: Why Now?
As highlighted in Generate: Biomedicines’s recent blog post, these women pioneers include Dr. Fiona Marshall, biotech founder and President of Biomedical Research at Novartis, and Dr. Mary Lou Jepsen, founder of Openwater and Pixel Qi. As generative biologists, they currently spearhead efforts to apply deep learning to an understanding of therapeutic molecules and to the design of these molecules from scratch. Their work demonstrates how AI can accelerate biomedical discovery and also how women’s leadership helps ensure inclusivity and creativity.
The Gender Gap—and the Growing Bridge
Historically, women have faced underrepresentation in both lab science and tech. The integration of AI into biology could easily have led to the same exclusion, except this time, women started showing up early, loudly, and influentially.
The Women in AI Canada panel, hosted by Invest Ottawa in March 2024, emphasized just how vital their presence is. The panelists—scientists, educators, and technologists from across North America—stressed the importance of mentorship, early STEM exposure for girls, and the dismantling of systemic biases that limit access to tech education.
As Dr. Ananya Shankar noted during the panel, “AI in science isn’t just about automation—it’s about collaboration. And collaboration thrives on diversity.”
Experimental Meets Computational: Real Impact
Scientists now experience a shrinking divide between the “wet lab” and the “dry lab.” Increasingly, experimental biologists learn to code and use bioinformatics tools to analyze highthroughput data, while computational scientists engage more with the biological context behind their models. This bidirectional learning not only expands individual skill sets, but it also fosters more integrated, collaborative science.
Women represent a growing part of this shift. While comprehensive global data remains limited, recent reports show increasing participation by women in both bioinformatics and biomedical data science, aided by targeted programs like the NIH’s Bridge2AI initiative and professional networks like Women in Bioinformatics and Data Science Latin America (WiBD-LA) and Women in Data Science (WiDS) Worldwide.
Artificial intelligence plays a key role in this transformation, not just as a research tool but also as a learning accelerator. Studies show that AI-powered platforms can enhance the pace of hypothesis generation, automate time-consuming data processing, and guide coding tasks in a way that significantly reduces the technical entry barrier for wet-lab scientists. As a result, researchers can spend less time wrangling raw data and more time focusing on biological questions—thereby uniting the two traditionally separate domains.
AI also plays a pivotal role in transforming modern biology by accelerating data analysis, guiding decision-making, and reducing technical barriers between disciplines. Women not only leverage these tools in scientific research; they also help to shape the future of AI itself, contributing as developers, ethicists, and innovators in biomedical AI systems.
A 2025 study in the International Journal of Science and Research Archive notes that interdisciplinary fluency among women scientists improves both the accuracy and the innovation of AI-biological research projects. Labs led by women also tend to apply AI ethically and with translational goals in mind, particularly in areas like women’s health and rare-disease modeling.
Building a Future, and Holding the Door Open
Women in AI biology focus on their own research, but they also create networks. Initiatives like WiBD, 500 Women Scientists, and Girls Who Code offer mentorship, workshops, and training platforms that uplift future scientists from diverse backgrounds. Importantly, these communities normalize leadership, innovation, and visibility for women—especially those who are Black, Indigenous, and people of color.
As the Generate Biomedicines piece notes, “We can’t just build the pipeline—we must keep the pipeline full, inclusive, and flexible.”
The Science of Inclusion
The future of biology doesn’t live in test tubes or datasets alone. It lives in the people who combine both, asking bold questions and daring to build new methods.
AI bridges biology’s past and future. Women not only cross that bridge: They reinforce it, guide others across, and work to design the next one.
Anushi Deraniyagala is a PhD candidate in Cellular Biology at the University of Georgia. She studies intracellular patterning in Tetrahymena thermophila, addressing one of biology’s most fundamental questions: how precise spatial patterns emerge inside cells—patterns that are critical for key cellular functions. Outside the lab, she enjoys writing, cooking, and exploring with her energetic toddler and lovely husband.
This article was originally published in AWIS Magazine. Join AWIS to access the full issue of AWIS Magazine and more member benefits.