Womb to World was one of the winning projects at the inaugural AI Sciencepreneurship Bootcamp, which supports early-stage researchers in translating AI research into real-world applications.
Developed by Dr Staci Weiss and Mo Vali, and pitched alongside Research Coordinator Ekaterina Moiseeva, the project explores how artificial intelligence could bring together fragmented women’s health data to enable more personalised, transparent fertility care. The platform is currently being co-designed with expectant parents and those navigating fertility treatment, ensuring it reflects lived experiences as well as clinical insight.
What did you learn from joining the AI Sciencepreneurship Bootcamp?
Participating in the bootcamp encouraged us to view our research through a different lens. As researchers, we often know there is a problem, but we do not always need to articulate why it matters beyond academic contexts. The bootcamp pushed us to think more explicitly about impact, demand, and how our research might translate into something that people would actively use.
It also highlighted the importance of storytelling around AI, and describing the tricky balance of how it can provide solutions which are at scale, yet highly tailored to the person. The bootcamp helped us imagine an audience beyond women’s health, to broader commercial horizons: we were able to communicate why a problem is worth attention and investment. Learning how to frame our work in terms of value, need, and real-world relevance has since become part of how we discuss the project.
What motivated the development of the Womb to World project and what problem is the project trying to address?
Womb to World emerged from a shared concern about how women experience fertility care and reproductive health more broadly. Across both clinical practice and psychological research, we repeatedly encountered the same issue: women often receive fragmented information about their bodies, particularly at critical moments such as fertility treatment.
For many, meaningful education about reproductive health begins only when something goes wrong - often at the point of IVF. By then, decisions are urgent, emotionally charged, and costly. We were interested in exploring how earlier, more integrated insight could support not only clinical outcomes, but also psychological wellbeing and a greater sense of agency throughout the fertility journey.
At its core, the project addresses both informational and structural gaps in fertility care. There is widespread misinformation around fertility timelines, IVF success rates, ovarian reserve, and menopause. Many women report never having been taught the fundamentals of how their reproductive systems function. At the same time, women’s health data is collected continuously across decades, but rarely brought together in a way that is meaningful for either clinicians or patients. Clinical records, ultrasound scans, self-reported symptoms, and family histories often remain siloed, resulting in generalised advice that may not reflect individual circumstances.
The Womb to World AI prototype responds to these challenges by using a short set of questions to understand where individuals are in their reproductive journey, then provides personalised guidance on appropriate next steps for seeking reproductive support. For those who are pregnant, the platform also uses ultrasound data to visualise a baby’s movements in the womb, an approach that can support parent–child bonding, and may be particularly valuable for parents who have experienced a complex path to pregnancy.
How does artificial intelligence feature in your approach and why is personalisation particularly important in fertility care?
The project uses a multimodal AI framework capable of learning from multiple types of data simultaneously, including electronic health records, routine ultrasound imaging, structured clinical information, and free-text patient accounts. Our intention is not to replace clinicians, but to support clinical decision-making. By learning from clinician-informed decision pathways and historical outcomes, the system can provide contextual insights that would be difficult to synthesise during a single consultation.
Fertility is highly individual, yet care pathways are often based on averages. Time, in particular, plays a critical role, but it does not affect everyone in the same way. Standardised advice may be appropriate for some, but harmful for others. Personalisation allows women to understand their fertility in relation to their own health history rather than relying on population-level statistics. Earlier access to personalised insight can support more informed decisions around family planning, fertility preservation, treatment timing, or alternative pathways.
What does being selected as a winning project mean for Womb to World?
Being selected as a winning project helped validate that this work resonates beyond academic settings. It reinforced that this is not only a technically interesting problem, but a socially significant one. The recognition also provided momentum to think more seriously about scale - how this work could move from research into practice, while remaining clinically responsible and ethically grounded. The next phase involves working with fertility clinics to establish a shared data consortium, enabling the system to be trained on a wider and more representative range of patient experiences.
Alongside technical development, we are also focused on public engagement - improving visibility, sharing progress transparently, and contributing to better fertility education. The aim is not only to build a technological solution, but to support more informed and empowered conversations around reproductive health.