Fertility
AI built for women, not just about women: Inside Ema’s groundbreaking pilot

By Morgan Rose, CNM, WHNP-BC, IBCLC | Chief Science Officer, Ema
AI is not a feature. It’s infrastructure.
That’s the thesis behind Ema’s newly launched Women’s Health AI Pilot, a fully funded programme aimed at rethinking how intelligence is integrated, not bolted on, across care, wellness, and diagnostics.
And it’s not hypothetical.
Five companies will be selected to co-design and deploy real use cases, powered by Ema’s clinically grounded, API-delivered AI platform.
The entire program is covered by Ema, underscoring their commitment to making thoughtful AI more accessible in women’s health.
The Opportunity
Applications are open through July 31, with five fully funded spots available. Ideal partners span:
- Wellness brands scaling smarter support
- Clinical platforms building triage or risk tools
- FemTech products in fertility, postpartum, or menopause
- Consumer health startups are ready to embed intelligence
What’s included:
- Custom use case design with Ema’s clinical and product team
- Full access to Ema’s AI API and design toolkit
- Strategic spotlighting via co-authored content and press
Why This Matters
As Ema’s CEO, Amanda Ducach, puts it:
“We’re not here to add another chatbot. We’re building the layer of intelligence that makes women’s health tools actually work safely, smartly, and built for how care happens in real life.”
Unlike generic models, Ema operates from a curated, clinically validated knowledge base.
Her AI is conflict-checked, human-reviewed, and deployable anywhere a woman is making a health decision, whether on a postpartum website, within a fertility app, or while deciding whether to spend $599 on red light therapy for menopausal symptoms.
Built for the Real World
Ema’s infrastructure already powers tools like PatientsLikeMe, delivering not just conversations, but:
- Behavioral data insights
- Clinical flagging and assessment flows
- Product fit engines and personalization
- QA systems like “conflict meter” to align brand content with current medical standards
This pilot isn’t just a test; it’s a partnership.
The goal: to help forward-thinking teams integrate clinical-grade AI and give women the tools that meet them where they are, smart, respectful, and rooted in real life.
For more information on how to bring AI to life inside your product, apply here.
Apply by July 31
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AI could transform ovarian care through personalisation, study finds

AI could transform ovarian care by personalising cancer and fertility treatment, but more clinical validation is needed before routine use.
A systematic review and meta-analysis found AI models showed high diagnostic accuracy for ovarian cancer when combining data such as ultrasound scans and blood test results.
Across 81 studies, AI models correctly identified ovarian cancer in around nine out of 10 cases, with pooled rates of 89 to 94 per cent.
They were also highly accurate at ruling out ovarian cancer when it was not present, with specificity of 85 to 91 per cent.
The analysis also found that explainable AI tools could predict complete surgical cytoreduction in advanced ovarian cancer.
Complete surgical cytoreduction means removing all visible cancer during surgery, which can be an important goal in treatment planning.
The tools achieved a pooled AUC of 0.87. AUC is a measure of how well a model distinguishes between different outcomes, with higher scores showing stronger performance.
In reproductive medicine, AI algorithms helped physicians optimise ovarian stimulation protocols and predict follicular growth during IVF.
Ovarian stimulation is the use of hormones to encourage the ovaries to produce eggs, while follicles are the small sacs in the ovaries where eggs develop.
The review found AI could reliably model ovarian response in IVF with a pooled AUC of 0.81.
However, researchers said challenges remain in translating promising research findings into routine clinical practice.
They identified substantial variation across studies, driven by retrospective study designs, variable AI systems and a lack of standardised validation.
Only 22 per cent of analysed studies reported prospective, multicentre external validation, where models are tested forward in time across multiple healthcare settings.
The authors called for rigorous validation to help close the gap between research and routine clinical practice, alongside standardised methodological and reporting frameworks, smooth integration with clinical workflow and robust governance to support responsible and ethical AI use.
They concluded: “Artificial intelligence is a transformative force in the management of ovarian conditions.
“In gynaecologic oncology, AI enhances every phase of care, from early detection and accurate diagnosis to prognostic stratification and surgical planning.”
In reproductive medicine, AI personalises ovarian stimulation and refines the diagnosis of heterogenous endocrine disorders such as PCOS.
PCOS, or polycystic ovary syndrome, is a hormonal condition that can affect periods, skin, weight and fertility.
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