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It is hardly news that women’s health has a research and data problem. Female biology and many conditions that disproportionately or differently affect women remain understudied, while the data that does exist is often incomplete, fragmented or not designed to answer the questions researchers are now asking.
But what feels much newer to me is the acceleration I’ve noticed recently in ideas and solutions - and the number of individuals and organisations across the ecosystem now trying to build the missing foundations.
This goes beyond the usual calls to close the data gap by doing more research involving women. Instead, what I’m seeing is a growing focus on research infrastructure - i.e, what needs to sit underneath that research in the first place. Do we have the right data? Does it contain the detail researchers actually need? Can different datasets work together? And do researchers and companies have the tools, standards and models they need to make sense of it?
It feels like we’ve reached something of a crunch point and more and more people across the ecosystem are coming to variations of the same conclusion. As ‘femtech’ moves into deeper tech, from drug discovery and precision diagnostics to advanced biosensors and AI, the weak foundations created by decades of under-research become harder to work around.
And fixing those weak foundations requires some creative thinking.
Building what is missing
One of the newest proposals comes from AthenaDAO founder Laura Minquini.
Her initiative, AthenaBIO, has just released a new thesis proposing a five-year programme focused initially on ovarian biology.
What’s interesting is how the thesis proposes to study the ovary. Instead of funding more individual research projects, the idea is to identiy around 15 leading laboratories already working on different parts of ovarian biology, and coordinate their work around some of the things the field is collectively missing. The labs would be funded to generate data and tools using common approaches, with the results becoming shared resources that other researchers can use rather than remaining within individual labs.
AthenaBIO hopes this would produce a reference map showing how the ovary changes across a woman’s lifetime; better biological markers for measuring ovarian function and ageing; agreed ways for researchers to measure and compare results across studies; and better models for testing potential new drugs and interventions.
The key is the coordination - making sure the research produces data and tools that can be shared and used across the wider field.
And for Laura, AI makes this work more urgent, not less. We know that AI can interrogate existing data in entirely new ways, but it can't compensate for data that was never collected, missing information or research that was never done.
So - all very important and compelling stuff. Except there’s just one obvious question….